Chapter 36
Train a multi-turn Wordle agent with GRPO on OpenEnv using Hugging Face Jobs
NotebookPython 333 cells
Train a multi-turn Wordle agent with GRPO on OpenEnv using Hugging Face Jobs
Authored by: Behrooz Azarkhalili
Agent RL is awkward to run in a notebook. GRPO holds the policy and generates the
rollouts, so a real run is hours of GPU time — too long for an interactive session, and it
dies with your kernel. This recipe shows the alternative: submit the notebook itself to
Hugging Face Jobs and
let papermill execute it non-interactively on HF's
cloud. You get the notebook's readability with a batch job's durability, and the executed
notebook — outputs and all — comes back as an artifact.
Multi-turn Wordle is the worked example: it runs on the official
openenv/wordle Space and needs no external
API keys.
Attribution. The environment wrapper and training setup build directly on TRL's official Wordle example (
openenv_wordle_grpo.ipynband the TRL OpenEnv guide). What this recipe adds is the non-interactive Jobs workflow around it — papermill submission,SMOKEgating, compute auto-detection, throughput calibration, and the environment-Space operational details below.
What you'll learn
- Submit a notebook to run non-interactively on Hugging Face Jobs with
papermill— the GPU work runs on HF's cloud, you only submit - Gate every expensive quantity behind a
SMOKEswitch so you prove the pipeline end-to-end in minutes before paying for a full run - Calibrate the full run from a measurement — time the smoke rollouts, project the real run, and size
--flavor/--timeout/steps from your own hardware instead of a guess - Wrap a multi-turn, stateful OpenEnv environment where each rollout spans up to 6
guesstool calls and the environment decides when the episode isdone - Keep training and evaluation prompts identical by deriving the tool schema from the environment class the same way TRL does
- Evaluate a multi-turn agent the faithful way — by playing complete games and measuring win rate
New to OpenEnv? For the single-turn case and the SFT-warm-start-then-GRPO story, see the OpenEnv SFT-warmup tutorial and the end-to-end walkthrough.
▶ Submit this notebook as an HF Job
Requires a positive credit balance (Jobs are pay-as-you-go — you pay only for the seconds you use). The control plane is plain HTTPS; the recipe below clones this notebook straight from the cookbook repo, so it runs as-is.
Start here — the smoke run. It exercises every cell end-to-end in minutes for a few cents, and prints the throughput measurement you need to size the real run.
bash
hf jobs run \
--flavor a10g-small \
--timeout 3600 \
--secrets HF_TOKEN \
-e SMOKE=1 -e REPORT_TO=trackio \
-e ENV_BASE_URL=https://openenv-wordle.hf.space \
python:3.12 \
bash -c "apt-get update -q && apt-get install -y -q git && \
pip install -q papermill ipykernel && \
python -m ipykernel install --user --name python3 && \
pip install -q trl openenv 'transformers>=5.3.0' trackio jmespath nest_asyncio datasets && \
GIT_LFS_SKIP_SMUDGE=1 git clone --depth 1 https://huggingface.co/spaces/openenv/wordle /wordle_env && \
pip install -q --no-deps /wordle_env && \
git clone --depth 1 https://github.com/huggingface/cookbook /cookbook && cd /cookbook/notebooks/en && \
papermill grpo_agent_wordle_hf_jobs.ipynb out.ipynb"--secrets HF_TOKEN forwards your token so the trained model can push to the Hub.
Then: the full run
The smoke run's final cell prints a measured projection — seconds per rollout on your GPU
and model, and the wall-clock the configured SMOKE=0 settings imply. Use those numbers to
set --flavor, --timeout, and GRPO_MAX_STEPS, rather than trusting the defaults below.
Then re-submit with -e SMOKE=0 and -e ENV_BASE_URL pointing at your own environment Space
(see the next section — this is required, not optional).
bash
hf jobs run \
--flavor a10g-small \
--timeout 10800 \
--secrets HF_TOKEN \
-e SMOKE=0 -e REPORT_TO=trackio \
-e GRPO_MAX_STEPS=<from the calibration cell> \
-e ENV_BASE_URL=https://<your-username>-wordle.hf.space \
python:3.12 \
bash -c "apt-get update -q && apt-get install -y -q git && \
pip install -q papermill ipykernel && \
python -m ipykernel install --user --name python3 && \
pip install -q trl openenv 'transformers>=5.3.0' trackio jmespath nest_asyncio datasets && \
GIT_LFS_SKIP_SMUDGE=1 git clone --depth 1 https://huggingface.co/spaces/openenv/wordle /wordle_env && \
pip install -q --no-deps /wordle_env && \
git clone --depth 1 https://github.com/huggingface/cookbook /cookbook && cd /cookbook/notebooks/en && \
papermill grpo_agent_wordle_hf_jobs.ipynb out.ipynb"⚠ Use your own environment Space for training runs
The Wordle environment is single-session by design, and the limit is not a tuning knob:
TextArenaEnvironmentinheritsSUPPORTS_CONCURRENT_SESSIONS = Falsefrom OpenEnv'sEnvironmentbase class- the Space builds its app without a concurrency override, so the server defaults to
max_concurrent_envs=1 - OpenEnv refuses to start a server with
max_concurrent_envs > 1for an environment not marked concurrent, so you cannot simply raise it
Consequences worth knowing before you submit a long job:
- The shared
openenv/wordleSpace serves one session at a time, community-wide. Fine for the smoke run above; not something to occupy for hours. - A crashed run leaks its session.
reset()opens a session and a crash never callsclose(), so failed attempts pile up until new connections are refused withServer at capacity: 1/1 sessions active (code: CAPACITY_REACHED). - Owning the Space is what lets you recover from (2) — you can restart it and clear leaked sessions. You cannot restart a Space you do not own.
So duplicate it once, and point ENV_BASE_URL at your copy:
python
from huggingface_hub import HfApi
api = HfApi()
api.duplicate_space("openenv/wordle") # -> <your-username>/wordle
# If a crashed run left it wedged, this clears every server-side session:
# api.restart_space("<your-username>/wordle")Your Space URL follows the pattern https://<owner>-<space-name>.hf.space, lowercased with
./_ replaced by -.
Faster rollouts with vLLM
An HF Job is a non-interactive runtime, so vLLM-accelerated generation works here (it is left off
in interactive notebooks because its init conflicts with IPython). Use a bigger GPU and install
vLLM in the same command — note it still needs ipykernel registered, since papermill runs the
notebook through a kernel either way:
bash
hf jobs run \
--flavor a100-large \
--timeout 21600 \
--secrets HF_TOKEN \
-e SMOKE=0 -e USE_VLLM=1 -e REPORT_TO=trackio \
-e ENV_BASE_URL=https://<your-username>-wordle.hf.space \
python:3.12 \
bash -c "apt-get update -q && apt-get install -y -q git && \
pip install -q papermill ipykernel vllm && \
python -m ipykernel install --user --name python3 && \
pip install -q trl openenv 'transformers>=5.3.0' trackio jmespath nest_asyncio datasets && \
GIT_LFS_SKIP_SMUDGE=1 git clone --depth 1 https://huggingface.co/spaces/openenv/wordle /wordle_env && \
pip install -q --no-deps /wordle_env && \
git clone --depth 1 https://github.com/huggingface/cookbook /cookbook && cd /cookbook/notebooks/en && \
papermill grpo_agent_wordle_hf_jobs.ipynb out.ipynb"0 · Install
We install TRL (the trainer), OpenEnv (the env client), and the environment's own package
(the Wordle env client ships from the openenv/wordle Space). transformers>=5.3.0 is
required because GRPO's environment_factory path — a TRL feature — depends on
tool-calling / chat-template behavior introduced in that Transformers release. vLLM stays
off in-notebook (its init conflicts with IPython) and is enabled only via USE_VLLM=1 on
an HF Job, where a non-interactive runtime makes it safe.
The install helper is idempotent: re-running this cell is a no-op, and so is running it
on a Job whose bash -c already installed the environment package. Its docstring records
why the env Space is cloned in full rather than installed with pip install git+….
In [1]python · cell 3
python
%pip install -q trl openenv "transformers>=5.3.0" trackio jmespath nest_asyncio datasets
import importlib.util
import os
import subprocess
import sys
from pathlib import Path
def ensure_env_package(space_id: str, module: str, workdir: str = "/tmp/openenv_spaces") -> None:
"""Install an OpenEnv environment client from its Hub Space, idempotently.
Re-running this cell is a no-op, and so is running it on an HF Job whose
`bash -c` already installed the package: if `module` imports, we return early.
We clone in full rather than letting pip do `git+https://...`, because pip
uses a partial ("promisor") clone and some Space repos are not served
reliably that way. Verified 2026-08-02:
$ git clone --filter=blob:none .../spaces/openenv/wordle
fatal: expected 'packfile'
fatal: could not fetch <sha> from promisor remote # empty worktree
while the identical command against .../spaces/sergiopaniego/reasoning_gym
exits 0. So this is per-Space behaviour, not a general property of HF Spaces
-- a full clone simply works for both. LFS blobs are skipped: environment
clients are pure Python, with no model weights to fetch.
"""
if importlib.util.find_spec(module) is not None:
print(f"'{module}' already importable — skipping install.")
return
dest = Path(workdir) / space_id.replace("/", "__")
if not dest.exists():
dest.parent.mkdir(parents=True, exist_ok=True)
subprocess.run(
["git", "clone", "--depth", "1",
f"https://huggingface.co/spaces/{space_id}", str(dest)],
check=True, env={**os.environ, "GIT_LFS_SKIP_SMUDGE": "1"},
)
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "--no-deps", str(dest)], check=True)
print(f"Installed '{module}' from Space '{space_id}'.")
# Both are env-overridable, so pointing this notebook at a different OpenEnv
# environment needs no code edit.
ENV_SPACE_ID = os.environ.get("ENV_SPACE_ID", "openenv/wordle")
ENV_MODULE = os.environ.get("ENV_MODULE", "textarena_env")
ensure_env_package(ENV_SPACE_ID, module=ENV_MODULE)Output
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. [notice] A new release of pip is available: 25.0.1 -> 26.2 [notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages. 'textarena_env' already importable — skipping install.
Why nest_asyncio?
The OpenEnv client is async (await env.reset(), await env.step()), but a Jupyter/Colab kernel already runs its own event loop. nest_asyncio.apply() lets us await inside notebook cells without RuntimeError: event loop already running.
💡 In a plain
.pyscript you'd useasyncio.run(...)instead — this shim is specifically for notebook runtimes.
In [2]python · cell 5
python
import nest_asyncio
nest_asyncio.apply()In [3]python · cell 6
python
# --- Authenticate with the Hugging Face Hub (portable) -------------------------
# Prefers an already-set HF_TOKEN (HF Jobs / Colab secret); falls back to the
# interactive widget. Never hard-codes a token.
if os.environ.get("HF_TOKEN"):
from huggingface_hub import login
login(token=os.environ["HF_TOKEN"])
print("Authenticated via HF_TOKEN.")
else:
try:
from huggingface_hub import notebook_login
notebook_login()
except Exception as exc: # non-interactive without a token
print(f"notebook_login unavailable ({exc}); set HF_TOKEN before running.")Output
/usr/local/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.
Authenticated via HF_TOKEN.
Resolve your Hub username
Downstream repo names (rollouts dataset, trained model) are built from your username, so we resolve it once via whoami() rather than hard-coding it. This keeps the notebook portable across accounts.
💡
whoami()reads the token you authenticated with above — no extra prompt.
In [4]python · cell 8
python
# Resolve your Hub username automatically — no hard-coded usernames downstream.
from huggingface_hub import whoami
try:
HF_USERNAME = whoami()["name"]
print(f"Hub user: {HF_USERNAME}")
except Exception as exc:
HF_USERNAME = os.environ.get("HF_USERNAME", "")
print(f"Could not resolve whoami() ({exc}); using HF_USERNAME={HF_USERNAME!r}.")Output
Hub user: ermiaazarkhalili
Auto-detect compute, pick a model that fits
Rather than hard-code a model, we read the GPU's VRAM and choose a size that fits (Qwen3-1.7B when there's headroom, else Qwen3-0.6B). Override with MODEL_NAME. This is what lets the same notebook run on a T4, an L4, or an A100 unchanged.
💡 GRPO holds the policy model and generates rollouts, so VRAM headroom matters more than for plain inference. TRL GRPO docs.
In [5]python · cell 10
python
# --- Auto-detect compute + pick a model that fits ------------------------------
# Larger model only when there is VRAM headroom; otherwise fall back. You can
# override by setting MODEL_NAME in the environment before launching.
import torch
if torch.cuda.is_available():
vram_gb = torch.cuda.get_device_properties(0).total_memory / 1024**3
DEVICE = "cuda"
else:
vram_gb = 0.0
DEVICE = "cpu"
DEFAULT_MODEL = "Qwen/Qwen3-1.7B" if vram_gb >= 24 else "Qwen/Qwen3-0.6B"
MODEL_NAME = os.environ.get("MODEL_NAME", DEFAULT_MODEL)
print(f"device={DEVICE} vram={vram_gb:.1f} GB -> MODEL_NAME={MODEL_NAME}")Output
device=cuda vram=22.3 GB -> MODEL_NAME=Qwen/Qwen3-0.6B
Run knobs: smoke vs. full
Every expensive quantity (rollout count, GRPO steps, eval size) is gated by SMOKE so you can prove the whole pipeline end-to-end in minutes (SMOKE=1) before committing to a real run (SMOKE=0). The values come from environment variables, so you never edit cells to change a run.
💡 This is the single switch that turns a 5-minute demo into a real training run.
In [6]python · cell 12
python
def _env_flag(name: str, default: str = "0") -> bool:
"""Read a boolean knob from the environment ('0'/'false' are false)."""
return os.environ.get(name, default) not in ("0", "false", "False")
def _env_int(name: str, default: int) -> int:
"""Read an integer knob from the environment, falling back to `default`."""
raw = os.environ.get(name)
return int(raw) if raw and raw.strip().lstrip("-").isdigit() else default
SMOKE = _env_flag("SMOKE", "1")
ENV_BASE_URL = os.environ.get("ENV_BASE_URL", "https://openenv-wordle.hf.space")
# Reference size of a real run. The calibration cell projects against these, and
# they are the SMOKE=0 defaults — one definition, no duplicated constants.
FULL_RUN = {"steps": 150, "grad_accum": 64}
# Every knob stays env-overridable, so the projection the calibration cell prints
# can be fed straight back in as `-e GRPO_MAX_STEPS=...` without editing a cell.
GRPO_MAX_STEPS = _env_int("GRPO_MAX_STEPS", 5 if SMOKE else FULL_RUN["steps"])
GRAD_ACCUM = _env_int("GRAD_ACCUM", 8 if SMOKE else FULL_RUN["grad_accum"])
N_EVAL_GAMES = _env_int("N_EVAL_GAMES", 3 if SMOKE else 20)
NUM_GENERATIONS = _env_int("NUM_GENERATIONS", 2)
# Single source of truth for chat-template rendering. Training passes this to
# GRPOConfig and evaluation passes the SAME dict to apply_chat_template, so the
# two can never drift apart.
CHAT_TEMPLATE_KWARGS = {"enable_thinking": False}
print(
f"SMOKE={SMOKE} grpo_steps={GRPO_MAX_STEPS} grad_accum={GRAD_ACCUM} "
f"num_generations={NUM_GENERATIONS} eval_games={N_EVAL_GAMES}\nenv={ENV_BASE_URL}"
)Output
SMOKE=True grpo_steps=5 grad_accum=8 num_generations=2 eval_games=3 env=https://ermiaazarkhalili-wordle.hf.space
1 · System prompt — teach the rules and the tool
The prompt states the Wordle rules, the feedback colour code, and — critically — that
the model must call the guess tool. The environment_factory loop drives tool calls,
so the model has to know the tool exists.
In [7]python · cell 14
python
WORDLE_PROMPT = """You are an expert Wordle solver with deep knowledge of English vocabulary, letter frequency patterns, and optimal guessing strategies.
Follow these rules to play Wordle:
1. The target is a 5-letter English word
2. You have 6 attempts to guess the correct word
3. After each guess, you receive color-coded feedback:
- GREEN (G): Letter is correct and in the correct position
- YELLOW (Y): Letter is in the word but in the wrong position
- GRAY (X): Letter is not in the word at all
4. All guesses must be valid 5-letter English words
5. You cannot reuse a word you've already guessed
6. Use the tool `guess` to make a guess.
"""2 · The multi-turn environment class
This wrapper follows TRL's official Wordle example. Two things make it multi-turn, and both are worth seeing explicitly if you have only met single-turn OpenEnv environments (as in the OpenEnv end-to-end walkthrough):
reset()returns the running feedback transcript, and the env appends new feedback each turn. We keepself._last_full_feedbackand slice out only the newly appended part so the model sees just the latest result.doneis set by the env, not by us — the game ends on a win or after 6 guesses, so a single rollout spans multipleguesstool calls. The trainer keeps stepping untildone=True.
guess is the one tool: TRL discovers it because it is a public method with a docstring.
It penalises invalid moves (reward 0) and otherwise records the env's reward.
The env_tools() helper at the bottom mirrors that same discovery rule so the evaluation
later can render an identical prompt — see §5.
In [8]python · cell 16
python
import inspect
from textarena_env import TextArenaAction, TextArenaEnv
# Terminal reward the environment pays for actually solving the word. TextArena's
# Wordle reward is binary at episode end: 1.0 for a solve, a partial value or 0.0
# when the episode ends any other way (notably, it terminates an agent that keeps
# submitting invalid moves and still pays out partial credit). So "reward > 0" is
# NOT a win test -- see the eval section.
ENV_SOLVED_REWARD = 1.0
class WordleEnv:
"""Multi-turn Wordle rollout: up to 6 `guess` tool calls until done."""
def __init__(self):
self.client = TextArenaEnv(base_url=ENV_BASE_URL).sync()
def reset(self, **kwargs) -> None | str:
result = self.client.reset()
# Env returns cumulative feedback; store the full text so we can diff each turn.
self._last_full_feedback = result.observation.messages[0].content
self.reward = 0.0
self.done = False
self.invalid = False
return self._last_full_feedback
def guess(self, guess: str) -> str:
"""Make a guess in the Wordle environment.
Args:
guess: The guessed word, formatted as '[abcde]'.
Returns:
The feedback message from the environment.
"""
if self.done:
raise ValueError("Game over.")
result = self.client.step(TextArenaAction(message=guess))
full = result.observation.messages[0].content
feedback = full[len(self._last_full_feedback):] # only the newly appended part
self._last_full_feedback = full
# Track rejected moves: the env answers a malformed guess with this notice and
# ends the episode if they keep coming. Surfacing the rate keeps a policy that
# is merely violating the rules from looking like a policy that is losing.
self.invalid = "You attempted an invalid move" in feedback
self.reward = 0.0 if self.invalid else result.reward
self.done = result.done
return feedback
def reward_func(environments, **kwargs) -> list[float]:
return [env.reward for env in environments]
def env_tools(env) -> list:
"""The tools TRL exposes to the policy for an OpenEnv environment instance.
`GRPOTrainer` builds its tool list by introspecting the environment instance:
every public bound method except `reset` and `get_reward` becomes a tool, and
it hands those *callables* straight to `apply_chat_template(tools=...)`, which
derives the JSON schema from each signature and docstring.
Reproducing that rule here — rather than hand-writing a schema — is what keeps
evaluation prompts byte-identical to the ones the policy was trained on. It
also names no tool, so swapping `WordleEnv` for any other OpenEnv wrapper
needs no edit: the spine stays, the env changes.
"""
return [
method
for name, method in inspect.getmembers(env, predicate=inspect.ismethod)
if name not in ("reset", "get_reward") and not name.startswith("_")
]3 · Dataset + GRPO config
The dataset is a stack of identical dummy prompts: its length sets the episode count,
because the real prompt for each rollout comes from env.reset() inside the factory.
Multi-turn games need a larger max_completion_length than a single-answer environment
(one completion has to hold a whole game) and a bigger gradient_accumulation_steps,
per TRL's Wordle example.
In [9]python · cell 18
python
from datasets import Dataset
N_PROMPTS = 30 if SMOKE else 3000
dataset = Dataset.from_dict(
{"prompt": [[{"role": "user", "content": WORDLE_PROMPT}] for _ in range(N_PROMPTS)]}
)Configure GRPO
GRPOConfig holds the RL hyperparameters. The agent-specific ones: num_generations (rollouts sampled per prompt — GRPO ranks them against each other), max_completion_length (room for the tool call), and report_to="trackio" for live charts. save_strategy/push_to_hub control persistence.
💡 GRPO is value-free: it needs no separate reward model — the environment's scalar reward is the only signal. GRPO paper.
In [10]python · cell 20
python
import re as _re
from trl import GRPOConfig
# Hyphen-lowercase slug for clean HF/trackio Space ids.
GRPO_OUT = _re.sub(r"-+", "-", _re.sub(r"[^a-z0-9]+", "-", f"wordle-grpo-{MODEL_NAME.split('/')[-1]}".lower())).strip("-")
# Logging backend knob: "trackio" (default) or "none" for a fully offline/headless run.
REPORT_TO = os.environ.get("REPORT_TO", "trackio")
_report_kwargs = {"report_to": REPORT_TO}
if REPORT_TO == "trackio":
_report_kwargs["trackio_space_id"] = GRPO_OUT
grpo_config = GRPOConfig(
num_train_epochs=1,
max_steps=GRPO_MAX_STEPS,
learning_rate=1e-6,
gradient_accumulation_steps=GRAD_ACCUM,
# Keep at 1. TRL grows its environment pool only when a batch needs more
# concurrent environment instances, and this env accepts a single session
# at a time (see the "own environment Space" note at the top).
per_device_train_batch_size=1,
warmup_steps=min(10, GRPO_MAX_STEPS),
optim="adamw_torch",
max_grad_norm=1.0,
num_generations=NUM_GENERATIONS,
max_completion_length=1024, # a whole multi-turn game, not one answer
log_completions=True,
num_completions_to_print=2,
chat_template_kwargs=CHAT_TEMPLATE_KWARGS, # shared with eval — see the knobs cell
output_dir=GRPO_OUT,
# Push weights to a repo DISTINCT from the Trackio Space id (=GRPO_OUT),
# otherwise push_to_hub sees the Trackio-owned repo and skips as 'no files
# modified'. A separate -model repo gets the actual checkpoint commit.
hub_model_id=f"{HF_USERNAME}/{GRPO_OUT}-model" if HF_USERNAME else f"{GRPO_OUT}-model",
logging_steps=1 if SMOKE else 10,
save_strategy="no",
gradient_checkpointing=True,
**_report_kwargs,
push_to_hub=not SMOKE,
# vLLM OFF in-notebook (IPython init). The optional cell below enables it for an HF Job.
)In [11]python · cell 21
python
# --- OPTIONAL: vLLM-accelerated GRPO rollouts (5-10x faster generation) ---------
# OFF by default: vLLM's init breaks under IPython/Jupyter, so leave USE_VLLM=0 for an
# in-notebook run. Enable it (USE_VLLM=1) ONLY in a non-IPython context: an HF Job
# or a fresh Colab runtime executed as a script. Colocate mode shares
# the single training GPU (right for Colab / one-GPU HF-Job flavors).
# Verified TRL v1.7.0 params: use_vllm, vllm_mode="colocate"|"server",
# vllm_gpu_memory_utilization. (server mode is multi-GPU; not used here.)
USE_VLLM = _env_flag("USE_VLLM", "0")
if USE_VLLM:
grpo_config.use_vllm = True
grpo_config.vllm_mode = "colocate"
# Leave room for the training copy of the model in colocate mode; tune per GPU/model.
grpo_config.vllm_gpu_memory_utilization = float(
os.environ.get("VLLM_GPU_MEM_UTIL", "0.3")
)
print(
f"[vLLM] enabled: mode=colocate gpu_mem_util={grpo_config.vllm_gpu_memory_utilization} "
"(requires a non-IPython runtime + `pip install vllm`)"
)
else:
print("[vLLM] disabled (USE_VLLM=0). In-notebook generation uses HF generate().")Output
[vLLM] disabled (USE_VLLM=0). In-notebook generation uses HF generate().
Train the agent
environment_factory=<EnvClass> is the key line: for each rollout the trainer creates an env (TRL may reuse env instances across a batch), generates the model's response, parses its tool call, steps the env, and reads the reward — the agent loop, automated.
Here we train directly from the base model — no SFT warm-start — so GRPO learns purely from the environment reward.
💡 This is what makes it agent training rather than text fine-tuning: the data is generated by the policy acting in the env, not read from a file.
In [12]python · cell 23
python
import time
from trl import GRPOTrainer
trainer = GRPOTrainer(
model=MODEL_NAME,
reward_funcs=reward_func,
train_dataset=dataset,
args=grpo_config,
environment_factory=WordleEnv,
)
# Wall-clock is the input to the calibration cell below — that is how the
# recommended full-run size gets measured instead of guessed.
_t0 = time.perf_counter()
trainer.train()
TRAIN_SECONDS = time.perf_counter() - _t0
print(f"training wall-clock: {TRAIN_SECONDS:.1f}s")
trainer.save_model(GRPO_OUT)
if not SMOKE:
trainer.push_to_hub(commit_message="GRPO fine-tune on Wordle (textarena)")Output
config.json: 0%| | 0.00/726 [00:00<?, ?B/s]
config.json: 100%|██████████| 726/726 [00:00<00:00, 3.09MB/s]
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/tmp/ipykernel_126/589940093.py:5: UserWarning: You are using 'environment_factory', which is an experimental feature. This API may change or be removed at any time without prior notice. Silence this warning by setting environment variable TRL_EXPERIMENTAL_SILENCE=1.
trainer = GRPOTrainer(
[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
<IPython.core.display.HTML object>
[5/5 00:58, Epoch 0/1]
| Step | Training Loss |
|---|---|
| 1 | 0.000010 |
| 2 | -0.006266 |
| 3 | -0.181215 |
| 4 | 0.034751 |
| 5 | -0.077280 |
╭──────────────────────────────────────────────────── Step 1 ─────────────────────────────────────────────────────╮
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │
│ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │
│ │ system │ <tool_call> │ 0.00 │ 0.00 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "apple"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ Y X X X G │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ [GAME] You have 5 guesses left. │ │ │ │
│ │ environment."}}} │ [GAME] You are currently at the 6th │ │ │ │
│ │ </tools> │ guess. │ │ │ │
│ │ │ [GAME] You have 1 more guess remaining. │ │ │ │
│ │ For each function call, return a json │ [GAME] You are about to make your final │ │ │ │
│ │ object with function name and arguments │ guess. │ │ │ │
│ │ within <tool_call></tool_call> XML │ [GAME] Did you have any other questions │ │ │ │
│ │ tags: │ or need further assistance? │ │ │ │
│ │ <tool_call> │ │ │ │ │
│ │ {"name": <function-name>, "arguments": │ │ │ │ │
│ │ <args-json-object>} │ │ │ │ │
│ │ </tool_call> │ │ │ │ │
│ │ user │ │ │ │ │
│ │ You are an expert Wordle solver with │ │ │ │ │
│ │ deep knowledge of English vocabulary, │ │ │ │ │
│ │ letter frequency patterns, and optimal │ │ │ │ │
│ │ guessing strategies. │ │ │ │ │
│ │ │ │ │ │ │
│ │ Follow these rules to play Wordle: │ │ │ │ │
│ │ │ │ │ │ │
│ │ 1. The target is a 5-letter English │ │ │ │ │
│ │ word │ │ │ │ │
│ │ 2. You have 6 attempts to guess the │ │ │ │ │
│ │ correct word │ │ │ │ │
│ │ 3. After each guess, you receive │ │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ │ │ │ │
│ │ in the correct position │ │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ │ │ │ │
│ │ but in the wrong position │ │ │ │ │
│ │ - GRAY (X): Letter is not in the │ │ │ │ │
│ │ word at all │ │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ │ │ │ │
│ │ English words │ │ │ │ │
│ │ 5. You cannot reuse a word you've │ │ │ │ │
│ │ already guessed │ │ │ │ │
│ │ 6. Use the tool `guess` to make a │ │ │ │ │
│ │ guess. │ │ │ │ │
│ │ │ │ │ │ │
│ │ [GAME] You are Playing Wordle. │ │ │ │ │
│ │ A secret 5-letter word has been chosen. │ │ │ │ │
│ │ You have 6 attempts to guess it. │ │ │ │ │
│ │ For each guess, wrap your word in │ │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ │ │ │ │
│ │ Feedback for each letter will be given │ │ │ │ │
│ │ as follows: │ │ │ │ │
│ │ - G (green): correct letter in the │ │ │ │ │
│ │ correct position │ │ │ │ │
│ │ - Y (yellow): letter exists in the │ │ │ │ │
│ │ word but in the wrong position │ │ │ │ │
│ │ - X (wrong): letter is not in the │ │ │ │ │
│ │ word │ │ │ │ │
│ │ Enter your guess to begin. │ │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ ├─────────────────────────────────────────┼─────────────────────────────────────────┼─────────────┼───────────┤ │
│ │ system │ <tool_call> │ 0.00 │ -0.71 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[apple]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ X X X X X │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │
│ │ </tools> │ {"guess": "[cube]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ For each function call, return a json │ user │ │ │ │
│ │ object with function name and arguments │ <tool_response> │ │ │ │
│ │ within <tool_call></tool_call> XML │ │ │ │ │
│ │ tags: │ [GAME] [cube] │ │ │ │
│ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │
│ │ {"name": <function-name>, "arguments": │ Reason: Your word must be exactly 5 │ │ │ │
│ │ <args-json-object>} │ letters. Please resubmit a valid move │ │ │ │
│ │ </tool_call> │ and remember to follow the game rules │ │ │ │
│ │ user │ to avoid penalties. │ │ │ │
│ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │
│ │ deep knowledge of English vocabulary, │ assistant │ │ │ │
│ │ letter frequency patterns, and optimal │ <think> │ │ │ │
│ │ guessing strategies. │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ Follow these rules to play Wordle: │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │
│ │ word │ {"guess": "[cat]"}} │ │ │ │
│ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │
│ │ correct word │ user │ │ │ │
│ │ 3. After each guess, you receive │ <tool_response> │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ [GAME] [cat] │ │ │ │
│ │ in the correct position │ </tool_response> │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ assistant │ │ │ │
│ │ but in the wrong position │ <think> │ │ │ │
│ │ - GRAY (X): Letter is not in the │ │ │ │ │
│ │ word at all │ </think> │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ │ │ │ │
│ │ English words │ <tool_call> │ │ │ │
│ │ 5. You cannot reuse a word you've │ {"name": "guess", "arguments": │ │ │ │
│ │ already guessed │ {"guess": "[dog]"}} │ │ │ │
│ │ 6. Use the tool `guess` to make a │ </tool_call> │ │ │ │
│ │ guess. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ [GAME] You are Playing Wordle. │ {'error': 'Game over.'} │ │ │ │
│ │ A secret 5-letter word has been chosen. │ </tool_response> │ │ │ │
│ │ You have 6 attempts to guess it. │ assistant │ │ │ │
│ │ For each guess, wrap your word in │ <think> │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ │ │ │ │
│ │ Feedback for each letter will be given │ </think> │ │ │ │
│ │ as follows: │ │ │ │ │
│ │ - G (green): correct letter in the │ [GAME] You have lost all attempts. │ │ │ │
│ │ correct position │ Please try a different word. │ │ │ │
│ │ - Y (yellow): letter exists in the │ │ │ │ │
│ │ word but in the wrong position │ │ │ │ │
│ │ - X (wrong): letter is not in the │ │ │ │ │
│ │ word │ │ │ │ │
│ │ Enter your guess to begin. │ │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ └─────────────────────────────────────────┴─────────────────────────────────────────┴─────────────┴───────────┘ │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────────── Step 1 ─────────────────────────────────────────────────────╮ │ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │ │ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │ │ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │ │ │ system │ <tool_call> │ 0.00 │ 0.00 │ │ │ │ # Tools │ {"name": "guess", "arguments": │ │ │ │ │ │ │ {"guess": "apple"}} │ │ │ │ │ │ You may call one or more functions to │ </tool_call> │ │ │ │ │ │ assist with the user query. │ user │ │ │ │ │ │ │ <tool_response> │ │ │ │ │ │ You are provided with function │ │ │ │ │ │ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │ │ │ tags: │ [GAME] You submitted [apple]. │ │ │ │ │ │ <tools> │ Feedback: │ │ │ │ │ │ {"type": "function", "function": │ A P P L E │ │ │ │ │ │ {"name": "guess", "description": "Make │ Y X X X G │ │ │ │ │ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │ │ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │ │ │ "properties": {"guess": {"type": │ assistant │ │ │ │ │ │ "string", "description": "The guessed │ <think> │ │ │ │ │ │ word, formatted as '[abcde]'."}}, │ │ │ │ │ │ │ "required": ["guess"]}, "return": │ </think> │ │ │ │ │ │ {"type": "string", "description": "The │ │ │ │ │ │ │ feedback message from the │ [GAME] You have 5 guesses left. │ │ │ │ │ │ environment."}}} │ [GAME] You are currently at the 6th │ │ │ │ │ │ </tools> │ guess. │ │ │ │ │ │ │ [GAME] You have 1 more guess remaining. │ │ │ │ │ │ For each function call, return a json │ [GAME] You are about to make your final │ │ │ │ │ │ object with function name and arguments │ guess. │ │ │ │ │ │ within <tool_call></tool_call> XML │ [GAME] Did you have any other questions │ │ │ │ │ │ tags: │ or need further assistance? │ │ │ │ │ │ <tool_call> │ │ │ │ │ │ │ {"name": <function-name>, "arguments": │ │ │ │ │ │ │ <args-json-object>} │ │ │ │ │ │ │ </tool_call> │ │ │ │ │ │ │ user │ │ │ │ │ │ │ You are an expert Wordle solver with │ │ │ │ │ │ │ deep knowledge of English vocabulary, │ │ │ │ │ │ │ letter frequency patterns, and optimal │ │ │ │ │ │ │ guessing strategies. │ │ │ │ │ │ │ │ │ │ │ │ │ │ Follow these rules to play Wordle: │ │ │ │ │ │ │ │ │ │ │ │ │ │ 1. The target is a 5-letter English │ │ │ │ │ │ │ word │ │ │ │ │ │ │ 2. You have 6 attempts to guess the │ │ │ │ │ │ │ correct word │ │ │ │ │ │ │ 3. After each guess, you receive │ │ │ │ │ │ │ color-coded feedback: │ │ │ │ │ │ │ - GREEN (G): Letter is correct and │ │ │ │ │ │ │ in the correct position │ │ │ │ │ │ │ - YELLOW (Y): Letter is in the word │ │ │ │ │ │ │ but in the wrong position │ │ │ │ │ │ │ - GRAY (X): Letter is not in the │ │ │ │ │ │ │ word at all │ │ │ │ │ │ │ 4. All guesses must be valid 5-letter │ │ │ │ │ │ │ English words │ │ │ │ │ │ │ 5. You cannot reuse a word you've │ │ │ │ │ │ │ already guessed │ │ │ │ │ │ │ 6. Use the tool `guess` to make a │ │ │ │ │ │ │ guess. │ │ │ │ │ │ │ │ │ │ │ │ │ │ [GAME] You are Playing Wordle. │ │ │ │ │ │ │ A secret 5-letter word has been chosen. │ │ │ │ │ │ │ You have 6 attempts to guess it. │ │ │ │ │ │ │ For each guess, wrap your word in │ │ │ │ │ │ │ square brackets (e.g., '[apple]'). │ │ │ │ │ │ │ Feedback for each letter will be given │ │ │ │
╭──────────────────────────────────────────────────── Step 2 ─────────────────────────────────────────────────────╮
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │
│ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │
│ │ system │ <tool_call> │ 0.00 │ 0.00 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[brown]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [brown] │ │ │ │
│ │ tags: │ [GAME] You submitted [brown]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ B R O W N │ │ │ │
│ │ {"name": "guess", "description": "Make │ X Y X X X │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │
│ │ </tools> │ {"guess": "[brown]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ For each function call, return a json │ user │ │ │ │
│ │ object with function name and arguments │ <tool_response> │ │ │ │
│ │ within <tool_call></tool_call> XML │ │ │ │ │
│ │ tags: │ [GAME] [brown] │ │ │ │
│ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │
│ │ {"name": <function-name>, "arguments": │ Reason: You have already guessed │ │ │ │
│ │ <args-json-object>} │ 'brown' before. Please try a different │ │ │ │
│ │ </tool_call> │ word. Please resubmit a valid move and │ │ │ │
│ │ user │ remember to follow the game rules to │ │ │ │
│ │ You are an expert Wordle solver with │ avoid penalties. │ │ │ │
│ │ deep knowledge of English vocabulary, │ </tool_response> │ │ │ │
│ │ letter frequency patterns, and optimal │ assistant │ │ │ │
│ │ guessing strategies. │ <think> │ │ │ │
│ │ │ │ │ │ │
│ │ Follow these rules to play Wordle: │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ 1. The target is a 5-letter English │ [GAME] [brown] │ │ │ │
│ │ word │ [GAME] You have already guessed 'brown' │ │ │ │
│ │ 2. You have 6 attempts to guess the │ twice. You have 5 guesses left. │ │ │ │
│ │ correct word │ The secret word is [ball]. │ │ │ │
│ │ 3. After each guess, you receive │ [GAME] You have 5 guesses left. │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ Please select a valid 5-letter word in │ │ │ │
│ │ in the correct position │ the 6th guess. │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ │ │ │ │
│ │ but in the wrong position │ │ │ │ │
│ │ - GRAY (X): Letter is not in the │ │ │ │ │
│ │ word at all │ │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ │ │ │ │
│ │ English words │ │ │ │ │
│ │ 5. You cannot reuse a word you've │ │ │ │ │
│ │ already guessed │ │ │ │ │
│ │ 6. Use the tool `guess` to make a │ │ │ │ │
│ │ guess. │ │ │ │ │
│ │ │ │ │ │ │
│ │ [GAME] You are Playing Wordle. │ │ │ │ │
│ │ A secret 5-letter word has been chosen. │ │ │ │ │
│ │ You have 6 attempts to guess it. │ │ │ │ │
│ │ For each guess, wrap your word in │ │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ │ │ │ │
│ │ Feedback for each letter will be given │ │ │ │ │
│ │ as follows: │ │ │ │ │
│ │ - G (green): correct letter in the │ │ │ │ │
│ │ correct position │ │ │ │ │
│ │ - Y (yellow): letter exists in the │ │ │ │ │
│ │ word but in the wrong position │ │ │ │ │
│ │ - X (wrong): letter is not in the │ │ │ │ │
│ │ word │ │ │ │ │
│ │ Enter your guess to begin. │ │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ ├─────────────────────────────────────────┼─────────────────────────────────────────┼─────────────┼───────────┤ │
│ │ system │ <tool_call> │ 0.30 │ 0.71 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[apple]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ X Y X X G │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │
│ │ </tools> │ {"guess": "[blueberry]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ For each function call, return a json │ user │ │ │ │
│ │ object with function name and arguments │ <tool_response> │ │ │ │
│ │ within <tool_call></tool_call> XML │ │ │ │ │
│ │ tags: │ [GAME] [blueberry] │ │ │ │
│ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │
│ │ {"name": <function-name>, "arguments": │ Reason: Your word must be exactly 5 │ │ │ │
│ │ <args-json-object>} │ letters. Please resubmit a valid move │ │ │ │
│ │ </tool_call> │ and remember to follow the game rules │ │ │ │
│ │ user │ to avoid penalties. │ │ │ │
│ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │
│ │ deep knowledge of English vocabulary, │ assistant │ │ │ │
│ │ letter frequency patterns, and optimal │ <think> │ │ │ │
│ │ guessing strategies. │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ Follow these rules to play Wordle: │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │
│ │ word │ {"guess": "[duck]"}} │ │ │ │
│ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │
│ │ correct word │ user │ │ │ │
│ │ 3. After each guess, you receive │ <tool_response> │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ [GAME] [duck] │ │ │ │
│ │ in the correct position │ </tool_response> │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ assistant │ │ │ │
│ │ but in the wrong position │ <think> │ │ │ │
│ │ - GRAY (X): Letter is not in the │ │ │ │ │
│ │ word at all │ </think> │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ │ │ │ │
│ │ English words │ <tool_call> │ │ │ │
│ │ 5. You cannot reuse a word you've │ {"name": "guess", "arguments": │ │ │ │
│ │ already guessed │ {"guess": "[carrot]"}} │ │ │ │
│ │ 6. Use the tool `guess` to make a │ </tool_call> │ │ │ │
│ │ guess. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ [GAME] You are Playing Wordle. │ {'error': 'Game over.'} │ │ │ │
│ │ A secret 5-letter word has been chosen. │ </tool_response> │ │ │ │
│ │ You have 6 attempts to guess it. │ assistant │ │ │ │
│ │ For each guess, wrap your word in │ <think> │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ │ │ │ │
│ │ Feedback for each letter will be given │ </think> │ │ │ │
│ │ as follows: │ │ │ │ │
│ │ - G (green): correct letter in the │ The game has ended. You have no more │ │ │ │
│ │ correct position │ guesses left. Let me know if you need │ │ │ │
│ │ - Y (yellow): letter exists in the │ further assistance! │ │ │ │
│ │ word but in the wrong position │ │ │ │ │
│ │ - X (wrong): letter is not in the │ │ │ │ │
│ │ word │ │ │ │ │
│ │ Enter your guess to begin. │ │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ └─────────────────────────────────────────┴─────────────────────────────────────────┴─────────────┴───────────┘ │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────────── Step 2 ─────────────────────────────────────────────────────╮ │ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │ │ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │ │ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │ │ │ system │ <tool_call> │ 0.00 │ 0.00 │ │ │ │ # Tools │ {"name": "guess", "arguments": │ │ │ │ │ │ │ {"guess": "[brown]"}} │ │ │ │ │ │ You may call one or more functions to │ </tool_call> │ │ │ │ │ │ assist with the user query. │ user │ │ │ │ │ │ │ <tool_response> │ │ │ │ │ │ You are provided with function │ │ │ │ │ │ │ signatures within <tools></tools> XML │ [GAME] [brown] │ │ │ │ │ │ tags: │ [GAME] You submitted [brown]. │ │ │ │ │ │ <tools> │ Feedback: │ │ │ │ │ │ {"type": "function", "function": │ B R O W N │ │ │ │ │ │ {"name": "guess", "description": "Make │ X Y X X X │ │ │ │ │ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │ │ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │ │ │ "properties": {"guess": {"type": │ assistant │ │ │ │ │ │ "string", "description": "The guessed │ <think> │ │ │ │ │ │ word, formatted as '[abcde]'."}}, │ │ │ │ │ │ │ "required": ["guess"]}, "return": │ </think> │ │ │ │ │ │ {"type": "string", "description": "The │ │ │ │ │ │ │ feedback message from the │ <tool_call> │ │ │ │ │ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │ │ │ </tools> │ {"guess": "[brown]"}} │ │ │ │ │ │ │ </tool_call> │ │ │ │ │ │ For each function call, return a json │ user │ │ │ │ │ │ object with function name and arguments │ <tool_response> │ │ │ │ │ │ within <tool_call></tool_call> XML │ │ │ │ │ │ │ tags: │ [GAME] [brown] │ │ │ │ │ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │ │ │ {"name": <function-name>, "arguments": │ Reason: You have already guessed │ │ │ │ │ │ <args-json-object>} │ 'brown' before. Please try a different │ │ │ │ │ │ </tool_call> │ word. Please resubmit a valid move and │ │ │ │ │ │ user │ remember to follow the game rules to │ │ │ │ │ │ You are an expert Wordle solver with │ avoid penalties. │ │ │ │ │ │ deep knowledge of English vocabulary, │ </tool_response> │ │ │ │ │ │ letter frequency patterns, and optimal │ assistant │ │ │ │ │ │ guessing strategies. │ <think> │ │ │ │ │ │ │ │ │ │ │ │ │ Follow these rules to play Wordle: │ </think> │ │ │ │ │ │ │ │ │ │ │ │ │ 1. The target is a 5-letter English │ [GAME] [brown] │ │ │ │ │ │ word │ [GAME] You have already guessed 'brown' │ │ │ │ │ │ 2. You have 6 attempts to guess the │ twice. You have 5 guesses left. │ │ │ │ │ │ correct word │ The secret word is [ball]. │ │ │ │ │ │ 3. After each guess, you receive │ [GAME] You have 5 guesses left. │ │ │ │ │ │ color-coded feedback: │ │ │ │ │ │ │ - GREEN (G): Letter is correct and │ Please select a valid 5-letter word in │ │ │ │ │ │ in the correct position │ the 6th guess. │ │ │ │ │ │ - YELLOW (Y): Letter is in the word │ │ │ │ │ │ │ but in the wrong position │ │ │ │ │ │ │ - GRAY (X): Letter is not in the │ │ │ │ │ │ │ word at all │ │ │ │ │ │ │ 4. All guesses must be valid 5-letter │ │ │ │ │ │ │ English words │ │ │ │ │ │ │ 5. You cannot reuse a word you've │ │ │ │ │ │ │ already guessed │ │ │ │ │ │ │ 6. Use the tool `guess` to make a │ │ │ │ │ │ │ guess. │ │ │ │ │ │ │ │ │ │ │ │ │ │ [GAME] You are Playing Wordle. │ │ │ │ │ │ │ A secret 5-letter word has been chosen. │ │ │ │ │ │ │ You have 6 attempts to guess it. │ │ │ │ │ │ │ For each guess, wrap your word in │ │ │ │ │ │ │ square brackets (e.g., '[apple]'). │ │ │ │ │ │ │ Feedback for each letter will be given │ │ │ │
╭──────────────────────────────────────────────────── Step 3 ─────────────────────────────────────────────────────╮
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │
│ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │
│ │ system │ <tool_call> │ 0.30 │ 0.00 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[shoelace]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [shoelace] │ │ │ │
│ │ tags: │ [GAME] You attempted an invalid move. │ │ │ │
│ │ <tools> │ Reason: Your word must be exactly 5 │ │ │ │
│ │ {"type": "function", "function": │ letters. Please resubmit a valid move │ │ │ │
│ │ {"name": "guess", "description": "Make │ and remember to follow the game rules │ │ │ │
│ │ a guess in the Wordle environment.", │ to avoid penalties. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │
│ │ </tools> │ {"guess": "[apple]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ For each function call, return a json │ user │ │ │ │
│ │ object with function name and arguments │ <tool_response> │ │ │ │
│ │ within <tool_call></tool_call> XML │ │ │ │ │
│ │ tags: │ [GAME] [apple] │ │ │ │
│ │ <tool_call> │ [GAME] You submitted [apple]. │ │ │ │
│ │ {"name": <function-name>, "arguments": │ Feedback: │ │ │ │
│ │ <args-json-object>} │ A P P L E │ │ │ │
│ │ </tool_call> │ Y Y X X X │ │ │ │
│ │ user │ You have 5 guesses left. │ │ │ │
│ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │
│ │ deep knowledge of English vocabulary, │ assistant │ │ │ │
│ │ letter frequency patterns, and optimal │ <think> │ │ │ │
│ │ guessing strategies. │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ Follow these rules to play Wordle: │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │
│ │ word │ {"guess": "[ball]"}} │ │ │ │
│ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │
│ │ correct word │ user │ │ │ │
│ │ 3. After each guess, you receive │ <tool_response> │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ [GAME] [ball] │ │ │ │
│ │ in the correct position │ [GAME] You attempted an invalid move. │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ Reason: Your word must be exactly 5 │ │ │ │
│ │ but in the wrong position │ letters. Please resubmit a valid move │ │ │ │
│ │ - GRAY (X): Letter is not in the │ and remember to follow the game rules │ │ │ │
│ │ word at all │ to avoid penalties. │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ </tool_response> │ │ │ │
│ │ English words │ assistant │ │ │ │
│ │ 5. You cannot reuse a word you've │ <think> │ │ │ │
│ │ already guessed │ │ │ │ │
│ │ 6. Use the tool `guess` to make a │ </think> │ │ │ │
│ │ guess. │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ [GAME] You are Playing Wordle. │ {"name": "guess", "arguments": │ │ │ │
│ │ A secret 5-letter word has been chosen. │ {"guess": "[water]"}} │ │ │ │
│ │ You have 6 attempts to guess it. │ </tool_call> │ │ │ │
│ │ For each guess, wrap your word in │ user │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ <tool_response> │ │ │ │
│ │ Feedback for each letter will be given │ │ │ │ │
│ │ as follows: │ [GAME] [water] │ │ │ │
│ │ - G (green): correct letter in the │ [GAME] You submitted [water]. │ │ │ │
│ │ correct position │ Feedback: │ │ │ │
│ │ - Y (yellow): letter exists in the │ W A T E R │ │ │ │
│ │ word but in the wrong position │ X G Y X X │ │ │ │
│ │ - X (wrong): letter is not in the │ You have 4 guesses left. │ │ │ │
│ │ word │ </tool_response> │ │ │ │
│ │ Enter your guess to begin. │ assistant │ │ │ │
│ │ │ <think> │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ <tool_call> │ │ │ │
│ │ │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[peach]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ │ │ │ │ │
│ │ │ [GAME] [peach] │ │ │ │
│ │ │ [GAME] You submitted [peach]. │ │ │ │
│ │ │ Feedback: │ │ │ │
│ │ │ P E A C H │ │ │ │
│ │ │ G X Y X X │ │ │ │
│ │ │ You have 3 guesses left. │ │ │ │
│ │ │ </tool_response> │ │ │ │
│ │ │ assistant │ │ │ │
│ │ │ <think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[gnarrel]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ │ │ │ │ │
│ │ │ [GAME] [gnarrel] │ │ │ │
│ │ │ [GAME] You attempted an invalid move. │ │ │ │
│ │ │ Reason: Your word must be exactly 5 │ │ │ │
│ │ │ letters. Please resubmit a valid move │ │ │ │
│ │ │ and remember to follow the game rules │ │ │ │
│ │ │ to avoid penalties. │ │ │ │
│ │ │ </tool_response> │ │ │ │
│ │ │ assistant │ │ │ │
│ │ │ <think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[ceilidrome]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ │ │ │ │ │
│ │ │ [GAME] [ceilidrome] │ │ │ │
│ │ │ </tool_response> │ │ │ │
│ │ │ assistant │ │ │ │
│ │ │ <think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[clef]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ │ {'error': 'Game over.'} │ │ │ │
│ │ │ </tool_response> │ │ │ │
│ │ │ assistant │ │ │ │
│ │ │ <think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ │ You've reached the 6th match. The │ │ │ │
│ │ │ correct word is [clef]. Good game! │ │ │ │
│ ├─────────────────────────────────────────┼─────────────────────────────────────────┼─────────────┼───────────┤ │
│ │ system │ <tool_call> │ 0.30 │ 0.71 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[apple]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ Y X X X G │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │
│ │ </tools> │ {"guess": "[batman]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ For each function call, return a json │ user │ │ │ │
│ │ object with function name and arguments │ <tool_response> │ │ │ │
│ │ within <tool_call></tool_call> XML │ │ │ │ │
│ │ tags: │ [GAME] [batman] │ │ │ │
│ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │
│ │ {"name": <function-name>, "arguments": │ Reason: Your word must be exactly 5 │ │ │ │
│ │ <args-json-object>} │ letters. Please resubmit a valid move │ │ │ │
│ │ </tool_call> │ and remember to follow the game rules │ │ │ │
│ │ user │ to avoid penalties. │ │ │ │
│ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │
│ │ deep knowledge of English vocabulary, │ assistant │ │ │ │
│ │ letter frequency patterns, and optimal │ <think> │ │ │ │
│ │ guessing strategies. │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ Follow these rules to play Wordle: │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │
│ │ word │ {"guess": "[beta]"}} │ │ │ │
│ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │
│ │ correct word │ user │ │ │ │
│ │ 3. After each guess, you receive │ <tool_response> │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ [GAME] [beta] │ │ │ │
│ │ in the correct position │ </tool_response> │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ assistant │ │ │ │
│ │ but in the wrong position │ <think> │ │ │ │
│ │ - GRAY (X): Letter is not in the │ │ │ │ │
│ │ word at all │ </think> │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ │ │ │ │
│ │ English words │ <tool_call> │ │ │ │
│ │ 5. You cannot reuse a word you've │ {"name": "guess", "arguments": │ │ │ │
│ │ already guessed │ {"guess": "[bwi]"}} │ │ │ │
│ │ 6. Use the tool `guess` to make a │ </tool_call> │ │ │ │
│ │ guess. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ [GAME] You are Playing Wordle. │ {'error': 'Game over.'} │ │ │ │
│ │ A secret 5-letter word has been chosen. │ </tool_response> │ │ │ │
│ │ You have 6 attempts to guess it. │ assistant │ │ │ │
│ │ For each guess, wrap your word in │ <think> │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ │ │ │ │
│ │ Feedback for each letter will be given │ </think> │ │ │ │
│ │ as follows: │ │ │ │ │
│ │ - G (green): correct letter in the │ [GAME] Game over. You have exhausted │ │ │ │
│ │ correct position │ all available attempts. The correct │ │ │ │
│ │ - Y (yellow): letter exists in the │ word was not guessed within your │ │ │ │
│ │ word but in the wrong position │ defined number of attempts. If you are │ │ │ │
│ │ - X (wrong): letter is not in the │ still interested in exploring other │ │ │ │
│ │ word │ possibilities, I'm happy to assist │ │ │ │
│ │ Enter your guess to begin. │ further! │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ └─────────────────────────────────────────┴─────────────────────────────────────────┴─────────────┴───────────┘ │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────────── Step 3 ─────────────────────────────────────────────────────╮ │ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │ │ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │ │ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │ │ │ system │ <tool_call> │ 0.30 │ 0.00 │ │ │ │ # Tools │ {"name": "guess", "arguments": │ │ │ │ │ │ │ {"guess": "[shoelace]"}} │ │ │ │ │ │ You may call one or more functions to │ </tool_call> │ │ │ │ │ │ assist with the user query. │ user │ │ │ │ │ │ │ <tool_response> │ │ │ │ │ │ You are provided with function │ │ │ │ │ │ │ signatures within <tools></tools> XML │ [GAME] [shoelace] │ │ │ │ │ │ tags: │ [GAME] You attempted an invalid move. │ │ │ │ │ │ <tools> │ Reason: Your word must be exactly 5 │ │ │ │ │ │ {"type": "function", "function": │ letters. Please resubmit a valid move │ │ │ │ │ │ {"name": "guess", "description": "Make │ and remember to follow the game rules │ │ │ │ │ │ a guess in the Wordle environment.", │ to avoid penalties. │ │ │ │ │ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │ │ │ "properties": {"guess": {"type": │ assistant │ │ │ │ │ │ "string", "description": "The guessed │ <think> │ │ │ │ │ │ word, formatted as '[abcde]'."}}, │ │ │ │ │ │ │ "required": ["guess"]}, "return": │ </think> │ │ │ │ │ │ {"type": "string", "description": "The │ │ │ │ │ │ │ feedback message from the │ <tool_call> │ │ │ │ │ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │ │ │ </tools> │ {"guess": "[apple]"}} │ │ │ │ │ │ │ </tool_call> │ │ │ │ │ │ For each function call, return a json │ user │ │ │ │ │ │ object with function name and arguments │ <tool_response> │ │ │ │ │ │ within <tool_call></tool_call> XML │ │ │ │ │ │ │ tags: │ [GAME] [apple] │ │ │ │ │ │ <tool_call> │ [GAME] You submitted [apple]. │ │ │ │ │ │ {"name": <function-name>, "arguments": │ Feedback: │ │ │ │ │ │ <args-json-object>} │ A P P L E │ │ │ │ │ │ </tool_call> │ Y Y X X X │ │ │ │ │ │ user │ You have 5 guesses left. │ │ │ │ │ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │ │ │ deep knowledge of English vocabulary, │ assistant │ │ │ │ │ │ letter frequency patterns, and optimal │ <think> │ │ │ │ │ │ guessing strategies. │ │ │ │ │ │ │ │ </think> │ │ │ │ │ │ Follow these rules to play Wordle: │ │ │ │ │ │ │ │ <tool_call> │ │ │ │ │ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │ │ │ word │ {"guess": "[ball]"}} │ │ │ │ │ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │ │ │ correct word │ user │ │ │ │ │ │ 3. After each guess, you receive │ <tool_response> │ │ │ │ │ │ color-coded feedback: │ │ │ │ │ │ │ - GREEN (G): Letter is correct and │ [GAME] [ball] │ │ │ │ │ │ in the correct position │ [GAME] You attempted an invalid move. │ │ │ │ │ │ - YELLOW (Y): Letter is in the word │ Reason: Your word must be exactly 5 │ │ │ │ │ │ but in the wrong position │ letters. Please resubmit a valid move │ │ │ │ │ │ - GRAY (X): Letter is not in the │ and remember to follow the game rules │ │ │ │ │ │ word at all │ to avoid penalties. │ │ │ │ │ │ 4. All guesses must be valid 5-letter │ </tool_response> │ │ │ │ │ │ English words │ assistant │ │ │ │ │ │ 5. You cannot reuse a word you've │ <think> │ │ │ │ │ │ already guessed │ │ │ │ │ │ │ 6. Use the tool `guess` to make a │ </think> │ │ │ │ │ │ guess. │ │ │ │ │ │ │ │ <tool_call> │ │ │ │ │ │ [GAME] You are Playing Wordle. │ {"name": "guess", "arguments": │ │ │ │ │ │ A secret 5-letter word has been chosen. │ {"guess": "[water]"}} │ │ │ │ │ │ You have 6 attempts to guess it. │ </tool_call> │ │ │ │ │ │ For each guess, wrap your word in │ user │ │ │ │ │ │ square brackets (e.g., '[apple]'). │ <tool_response> │ │ │ │ │ │ Feedback for each letter will be given │ │ │
╭──────────────────────────────────────────────────── Step 4 ─────────────────────────────────────────────────────╮
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │
│ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │
│ │ system │ <tool_call> │ 0.10 │ 0.71 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[apple]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ X X X X Y │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │
│ │ </tools> │ {"guess": "[carpet]"}} │ │ │ │
│ │ │ </tool_call> │ │ │ │
│ │ For each function call, return a json │ user │ │ │ │
│ │ object with function name and arguments │ <tool_response> │ │ │ │
│ │ within <tool_call></tool_call> XML │ │ │ │ │
│ │ tags: │ [GAME] [carpet] │ │ │ │
│ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │
│ │ {"name": <function-name>, "arguments": │ Reason: Your word must be exactly 5 │ │ │ │
│ │ <args-json-object>} │ letters. Please resubmit a valid move │ │ │ │
│ │ </tool_call> │ and remember to follow the game rules │ │ │ │
│ │ user │ to avoid penalties. │ │ │ │
│ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │
│ │ deep knowledge of English vocabulary, │ assistant │ │ │ │
│ │ letter frequency patterns, and optimal │ <think> │ │ │ │
│ │ guessing strategies. │ │ │ │ │
│ │ │ </think> │ │ │ │
│ │ Follow these rules to play Wordle: │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │
│ │ word │ {"guess": "[treehouse]"}} │ │ │ │
│ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │
│ │ correct word │ user │ │ │ │
│ │ 3. After each guess, you receive │ <tool_response> │ │ │ │
│ │ color-coded feedback: │ │ │ │ │
│ │ - GREEN (G): Letter is correct and │ [GAME] [treehouse] │ │ │ │
│ │ in the correct position │ </tool_response> │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ assistant │ │ │ │
│ │ but in the wrong position │ <think> │ │ │ │
│ │ - GRAY (X): Letter is not in the │ │ │ │ │
│ │ word at all │ </think> │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ │ │ │ │
│ │ English words │ <tool_call> │ │ │ │
│ │ 5. You cannot reuse a word you've │ {"name": "guess", "arguments": │ │ │ │
│ │ already guessed │ {"guess": "[broadstick]"}} │ │ │ │
│ │ 6. Use the tool `guess` to make a │ </tool_call> │ │ │ │
│ │ guess. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ [GAME] You are Playing Wordle. │ {'error': 'Game over.'} │ │ │ │
│ │ A secret 5-letter word has been chosen. │ </tool_response> │ │ │ │
│ │ You have 6 attempts to guess it. │ assistant │ │ │ │
│ │ For each guess, wrap your word in │ <think> │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ │ │ │ │
│ │ Feedback for each letter will be given │ </think> │ │ │ │
│ │ as follows: │ │ │ │ │
│ │ - G (green): correct letter in the │ [GAME] You have reached 6 attempts. │ │ │ │
│ │ correct position │ [GAME] Your word has been guessed. You │ │ │ │
│ │ - Y (yellow): letter exists in the │ can only play one more time. │ │ │ │
│ │ word but in the wrong position │ [GAME] You attempt at least 6 guesses. │ │ │ │
│ │ - X (wrong): letter is not in the │ Please try again. │ │ │ │
│ │ word │ │ │ │ │
│ │ Enter your guess to begin. │ │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ ├─────────────────────────────────────────┼─────────────────────────────────────────┼─────────────┼───────────┤ │
│ │ system │ <tool_call> │ 0.10 │ -0.71 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[apple]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ Y X X X X │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ [GAME] You have 1 guess left. │ │ │ │
│ │ environment."}}} │ [GAME] Let me try to guess the word. │ │ │ │
│ │ </tools> │ │ │ │ │
│ │ │ <tool_call> │ │ │ │
│ │ For each function call, return a json │ {"name": "guess", "arguments": │ │ │ │
│ │ object with function name and arguments │ {"guess": "[xyzabc]"}} │ │ │ │
│ │ within <tool_call></tool_call> XML │ </tool_call> │ │ │ │
│ │ tags: │ user │ │ │ │
│ │ <tool_call> │ <tool_response> │ │ │ │
│ │ {"name": <function-name>, "arguments": │ │ │ │ │
│ │ <args-json-object>} │ [GAME] [xyzabc] │ │ │ │
│ │ </tool_call> │ [GAME] You attempted an invalid move. │ │ │ │
│ │ user │ Reason: Your word must be exactly 5 │ │ │ │
│ │ You are an expert Wordle solver with │ letters. Please resubmit a valid move │ │ │ │
│ │ deep knowledge of English vocabulary, │ and remember to follow the game rules │ │ │ │
│ │ letter frequency patterns, and optimal │ to avoid penalties. │ │ │ │
│ │ guessing strategies. │ </tool_response> │ │ │ │
│ │ │ assistant │ │ │ │
│ │ Follow these rules to play Wordle: │ <think> │ │ │ │
│ │ │ │ │ │ │
│ │ 1. The target is a 5-letter English │ </think> │ │ │ │
│ │ word │ │ │ │ │
│ │ 2. You have 6 attempts to guess the │ [GAME] Let me try again with a valid │ │ │ │
│ │ correct word │ word. │ │ │ │
│ │ 3. After each guess, you receive │ │ │ │ │
│ │ color-coded feedback: │ <tool_call> │ │ │ │
│ │ - GREEN (G): Letter is correct and │ {"name": "guess", "arguments": │ │ │ │
│ │ in the correct position │ {"guess": "[xyzem]"}} │ │ │ │
│ │ - YELLOW (Y): Letter is in the word │ </tool_call> │ │ │ │
│ │ but in the wrong position │ user │ │ │ │
│ │ - GRAY (X): Letter is not in the │ <tool_response> │ │ │ │
│ │ word at all │ │ │ │ │
│ │ 4. All guesses must be valid 5-letter │ [GAME] [xyzem] │ │ │ │
│ │ English words │ </tool_response> │ │ │ │
│ │ 5. You cannot reuse a word you've │ assistant │ │ │ │
│ │ already guessed │ <think> │ │ │ │
│ │ 6. Use the tool `guess` to make a │ │ │ │ │
│ │ guess. │ </think> │ │ │ │
│ │ │ │ │ │ │
│ │ [GAME] You are Playing Wordle. │ [GAME] [xyzem] │ │ │ │
│ │ A secret 5-letter word has been chosen. │ [GAME] You have 0 guesses left. │ │ │ │
│ │ You have 6 attempts to guess it. │ [GAME] You've completed the guessing │ │ │ │
│ │ For each guess, wrap your word in │ round. The correct word is [xyzem], and │ │ │ │
│ │ square brackets (e.g., '[apple]'). │ you've made the appropriate number of │ │ │ │
│ │ Feedback for each letter will be given │ guesses. Make sure to follow the rules │ │ │ │
│ │ as follows: │ to continue playing Wordle. │ │ │ │
│ │ - G (green): correct letter in the │ │ │ │ │
│ │ correct position │ │ │ │ │
│ │ - Y (yellow): letter exists in the │ │ │ │ │
│ │ word but in the wrong position │ │ │ │ │
│ │ - X (wrong): letter is not in the │ │ │ │ │
│ │ word │ │ │ │ │
│ │ Enter your guess to begin. │ │ │ │ │
│ │ │ │ │ │ │
│ │ assistant │ │ │ │ │
│ │ <think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ </think> │ │ │ │ │
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ └─────────────────────────────────────────┴─────────────────────────────────────────┴─────────────┴───────────┘ │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────────── Step 4 ─────────────────────────────────────────────────────╮ │ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │ │ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │ │ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │ │ │ system │ <tool_call> │ 0.10 │ 0.71 │ │ │ │ # Tools │ {"name": "guess", "arguments": │ │ │ │ │ │ │ {"guess": "[apple]"}} │ │ │ │ │ │ You may call one or more functions to │ </tool_call> │ │ │ │ │ │ assist with the user query. │ user │ │ │ │ │ │ │ <tool_response> │ │ │ │ │ │ You are provided with function │ │ │ │ │ │ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │ │ │ tags: │ [GAME] You submitted [apple]. │ │ │ │ │ │ <tools> │ Feedback: │ │ │ │ │ │ {"type": "function", "function": │ A P P L E │ │ │ │ │ │ {"name": "guess", "description": "Make │ X X X X Y │ │ │ │ │ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │ │ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │ │ │ "properties": {"guess": {"type": │ assistant │ │ │ │ │ │ "string", "description": "The guessed │ <think> │ │ │ │ │ │ word, formatted as '[abcde]'."}}, │ │ │ │ │ │ │ "required": ["guess"]}, "return": │ </think> │ │ │ │ │ │ {"type": "string", "description": "The │ │ │ │ │ │ │ feedback message from the │ <tool_call> │ │ │ │ │ │ environment."}}} │ {"name": "guess", "arguments": │ │ │ │ │ │ </tools> │ {"guess": "[carpet]"}} │ │ │ │ │ │ │ </tool_call> │ │ │ │ │ │ For each function call, return a json │ user │ │ │ │ │ │ object with function name and arguments │ <tool_response> │ │ │ │ │ │ within <tool_call></tool_call> XML │ │ │ │ │ │ │ tags: │ [GAME] [carpet] │ │ │ │ │ │ <tool_call> │ [GAME] You attempted an invalid move. │ │ │ │ │ │ {"name": <function-name>, "arguments": │ Reason: Your word must be exactly 5 │ │ │ │ │ │ <args-json-object>} │ letters. Please resubmit a valid move │ │ │ │ │ │ </tool_call> │ and remember to follow the game rules │ │ │ │ │ │ user │ to avoid penalties. │ │ │ │ │ │ You are an expert Wordle solver with │ </tool_response> │ │ │ │ │ │ deep knowledge of English vocabulary, │ assistant │ │ │ │ │ │ letter frequency patterns, and optimal │ <think> │ │ │ │ │ │ guessing strategies. │ │ │ │ │ │ │ │ </think> │ │ │ │ │ │ Follow these rules to play Wordle: │ │ │ │ │ │ │ │ <tool_call> │ │ │ │ │ │ 1. The target is a 5-letter English │ {"name": "guess", "arguments": │ │ │ │ │ │ word │ {"guess": "[treehouse]"}} │ │ │ │ │ │ 2. You have 6 attempts to guess the │ </tool_call> │ │ │ │ │ │ correct word │ user │ │ │ │ │ │ 3. After each guess, you receive │ <tool_response> │ │ │ │ │ │ color-coded feedback: │ │ │ │ │ │ │ - GREEN (G): Letter is correct and │ [GAME] [treehouse] │ │ │ │ │ │ in the correct position │ </tool_response> │ │ │ │ │ │ - YELLOW (Y): Letter is in the word │ assistant │ │ │ │ │ │ but in the wrong position │ <think> │ │ │ │ │ │ - GRAY (X): Letter is not in the │ │ │ │ │ │ │ word at all │ </think> │ │ │ │ │ │ 4. All guesses must be valid 5-letter │ │ │ │ │ │ │ English words │ <tool_call> │ │ │ │ │ │ 5. You cannot reuse a word you've │ {"name": "guess", "arguments": │ │ │ │ │ │ already guessed │ {"guess": "[broadstick]"}} │ │ │ │ │ │ 6. Use the tool `guess` to make a │ </tool_call> │ │ │ │ │ │ guess. │ user │ │ │ │ │ │ │ <tool_response> │ │ │ │ │ │ [GAME] You are Playing Wordle. │ {'error': 'Game over.'} │ │ │ │ │ │ A secret 5-letter word has been chosen. │ </tool_response> │ │ │ │ │ │ You have 6 attempts to guess it. │ assistant │ │ │ │ │ │ For each guess, wrap your word in │ <think> │ │ │ │ │ │ square brackets (e.g., '[apple]'). │ │ │ │ │ │ │ Feedback for each letter will be given │ </think> │ │
╭──────────────────────────────────────────────────── Step 5 ─────────────────────────────────────────────────────╮
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │
│ ┃ Prompt ┃ Completion ┃ reward_func ┃ Advantage ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │
│ │ system │ <tool_call> │ 0.10 │ -0.71 │ │
│ │ # Tools │ {"name": "guess", "arguments": │ │ │ │
│ │ │ {"guess": "[apple]"}} │ │ │ │
│ │ You may call one or more functions to │ </tool_call> │ │ │ │
│ │ assist with the user query. │ user │ │ │ │
│ │ │ <tool_response> │ │ │ │
│ │ You are provided with function │ │ │ │ │
│ │ signatures within <tools></tools> XML │ [GAME] [apple] │ │ │ │
│ │ tags: │ [GAME] You submitted [apple]. │ │ │ │
│ │ <tools> │ Feedback: │ │ │ │
│ │ {"type": "function", "function": │ A P P L E │ │ │ │
│ │ {"name": "guess", "description": "Make │ Y X X X X │ │ │ │
│ │ a guess in the Wordle environment.", │ You have 5 guesses left. │ │ │ │
│ │ "parameters": {"type": "object", │ </tool_response> │ │ │ │
│ │ "properties": {"guess": {"type": │ assistant │ │ │ │
│ │ "string", "description": "The guessed │ <think> │ │ │ │
│ │ word, formatted as '[abcde]'."}}, │ │ │ │ │
│ │ "required": ["guess"]}, "return": │ </think> │ │ │ │
│ │ {"type": "string", "description": "The │ │ │ │ │
│ │ feedback message from the │ <tool_call> │ │ │ │
│ │ environment."}}} │ {"name": "guess",
…[输出过长，已省略]
Calibrate the full run from this one
Rollout cost depends on the GPU, the policy size, and whether vLLM is on — so a
recommended step count that isn't measured on your hardware is just a guess. This
cell divides the training wall-clock by the rollouts actually consumed, then projects
the full run and reports the largest GRPO_MAX_STEPS that fits each --timeout.
💡 Run this under
SMOKE=1first. The numbers it prints are what you feed into theSMOKE=0submission —-e GRPO_MAX_STEPS=…plus a matching--flavor/--timeout.
In [13]python · cell 25
python
def seconds_per_rollout(train_seconds: float, steps: int, grad_accum: int) -> float:
"""Measured cost of a single rollout.
An optimizer step consumes `grad_accum` rollouts, so a run of `steps` steps
consumes `steps * grad_accum` of them in total.
"""
return train_seconds / max(1, steps * grad_accum)
def project_seconds(sec_per_rollout: float, steps: int, grad_accum: int) -> float:
"""Wall-clock a run of this size implies, at the measured per-rollout cost."""
return sec_per_rollout * steps * grad_accum
def max_steps_within(budget_seconds: float, sec_per_rollout: float, grad_accum: int,
headroom: float = 0.75) -> int:
"""Largest step count fitting a timeout, leaving headroom for setup and eval."""
return max(1, int((budget_seconds * headroom) / (sec_per_rollout * grad_accum)))
def _hms(seconds: float) -> str:
return f"{seconds / 3600:.1f}h" if seconds >= 3600 else f"{seconds / 60:.1f}min"
sec_rollout = seconds_per_rollout(TRAIN_SECONDS, GRPO_MAX_STEPS, GRAD_ACCUM)
rollouts_done = GRPO_MAX_STEPS * GRAD_ACCUM
print(f"measured: {TRAIN_SECONDS:.1f}s over {rollouts_done} rollouts "
f"-> {sec_rollout:.2f}s per rollout (model={MODEL_NAME}, vram={vram_gb:.0f}GB, vllm={USE_VLLM})")
full_steps, full_ga = FULL_RUN["steps"], FULL_RUN["grad_accum"]
projected = project_seconds(sec_rollout, full_steps, full_ga)
print(f"\nprojected full run ({full_steps} steps x {full_ga} grad-accum "
f"= {full_steps * full_ga:,} rollouts): {_hms(projected)}")
print("\nSteps that actually fit a given --timeout on THIS hardware:")
for budget in (3600, 10800, 21600, 43200):
fits = max_steps_within(budget, sec_rollout, full_ga)
print(f" --timeout {budget:>6} ({_hms(budget):>5}) -> GRPO_MAX_STEPS <= {fits}"
f" [{'full run fits' if projected <= budget else 'full run does NOT fit'}]")
print("\nRe-submit the full run with -e GRPO_MAX_STEPS=<value from the row you chose>.")
print("A bigger --flavor or -e USE_VLLM=1 lowers seconds-per-rollout; re-run this cell to re-measure.")Output
measured: 92.4s over 40 rollouts -> 2.31s per rollout (model=Qwen/Qwen3-0.6B, vram=22GB, vllm=False) projected full run (150 steps x 64 grad-accum = 9,600 rollouts): 6.2h Steps that actually fit a given --timeout on THIS hardware: --timeout 3600 ( 1.0h) -> GRPO_MAX_STEPS <= 18 [full run does NOT fit] --timeout 10800 ( 3.0h) -> GRPO_MAX_STEPS <= 54 [full run does NOT fit] --timeout 21600 ( 6.0h) -> GRPO_MAX_STEPS <= 109 [full run does NOT fit] --timeout 43200 (12.0h) -> GRPO_MAX_STEPS <= 219 [full run fits] Re-submit the full run with -e GRPO_MAX_STEPS=<value from the row you chose>. A bigger --flavor or -e USE_VLLM=1 lowers seconds-per-rollout; re-run this cell to re-measure.
4 · Training signal — reward delta
A quick sanity check: compare mean reward over the first few logged steps against the last few. This tells you whether reward moved during training — it is not a held-out quality claim. The honest quality measure comes in §5, by playing full games.
In [14]python · cell 27
python
import statistics
rewards = [log["reward"] for log in trainer.state.log_history if "reward" in log]
if len(rewards) < 5:
print(f"Only {len(rewards)} reward logs — increase max_steps for a clean delta.")
else:
initial, final = statistics.mean(rewards[:5]), statistics.mean(rewards[-5:])
print(f"initial={initial:.2%} final={final:.2%} delta={(final - initial) * 100:+.2f}pp")Output
initial=13.00% final=13.00% delta=+0.00pp
5 · Test the trained agent — play full games
For a multi-turn agent the faithful test is playing complete games: the agent acts
turn by turn against live env feedback until the episode ends. We load the trained model,
let it guess turn-by-turn, and measure the solve rate over N_EVAL_GAMES.
Two traps are worth naming, because both quietly turn this measurement into a fiction.
1 · The prompt has to match training. During training TRL renders each prompt with the
environment's tool schema attached; if evaluation renders it without, the policy is
judged on a format it was never optimised for, and a lenient text parser hides that by
still extracting a guess. So we pass tools=env_tools(env) — the same discovery rule TRL
uses — plus the same CHAT_TEMPLATE_KWARGS, and report how many turns came from real tool
calls, so any residual mismatch is visible in the output.
2 · "Reward > 0" is not a win. TextArena pays its terminal success reward (1.0) for
actually solving the word, but it also ends an episode early when the agent keeps
submitting invalid moves — and still pays partial credit. Scoring env.reward > 0 as a win
therefore counts rule-breaking as success, and a small policy that guesses six-letter words
can post a perfect score having never solved anything. We test against
ENV_SOLVED_REWARD instead, and report an invalid-move rate alongside, so a policy
that is breaking the rules looks different from one that is merely losing.
💡 A metric that can only move in the flattering direction is not a measurement. Whenever an environment hands you a scalar, check what it pays out on the failure paths before you treat "positive" as "good".
In [15]python · cell 29
python
import json
import re
from collections import Counter
from transformers import AutoModelForCausalLM, AutoTokenizer
_BRACKETED = re.compile(r"\[([A-Za-z]+)\]")
def parse_guess(text: str) -> tuple[str, str]:
"""Pull the guessed word out of one model turn.
Returns `(word, how)` where `how` is one of:
"tool_call" — a well-formed tool call, i.e. the format training used
"regex" — recovered from bare `[abcde]` text
"raw" — last-resort slice of the raw output
The `how` tally matters: a large non-`tool_call` share means the eval prompt
is not rendering the format the policy was trained on, and the score is
measuring prompt mismatch rather than agent skill.
The word is returned WITHOUT brackets — the tool contract asks the model for
`[abcde]`, so the tool-call path hands back an already-bracketed string and
the caller would otherwise re-wrap it into `[[abcde]]`. We deliberately do
NOT repair a wrong-length word: a policy that guesses six letters is making
an invalid move, and the eval should report that rather than launder it.
"""
def _strip(word: str) -> str:
m = _BRACKETED.search(word)
return (m.group(1) if m else word).strip()
if "{" in text and "}" in text:
try:
payload = json.loads(text[text.index("{"): text.rindex("}") + 1])
args = payload.get("arguments", payload)
word = (args or {}).get("guess", "")
if word:
return _strip(str(word)), "tool_call"
except (ValueError, AttributeError):
pass
match = _BRACKETED.search(text)
if match:
return match.group(1), "regex"
return text.strip()[:5], "raw"
def play_one_game(env, model, tokenizer, verbose=False) -> dict:
"""Play one Wordle game on an EXISTING env; return a per-game record.
The env is created once by the caller and reused across games (`reset()` starts
a fresh game). We do NOT close it here — closing the shared client mid-loop
tears down the WebSocket the next game's `reset()` needs (ConnectionClosedOK).
"""
obs = env.reset()
messages = [{"role": "user", "content": WORDLE_PROMPT}]
if obs:
messages.append({"role": "user", "content": obs})
how_tally: Counter = Counter()
turns = invalid = 0
for _turn in range(6):
if env.done:
break
# Same tool schema and same template kwargs the trainer used, so the model
# sees at eval exactly the prompt format it was optimised on.
prompt_text = tokenizer.apply_chat_template(
messages,
tools=env_tools(env) or None, # `or None`: an empty list still renders tool boilerplate
add_generation_prompt=True,
tokenize=False,
**CHAT_TEMPLATE_KWARGS,
)
inputs = tokenizer([prompt_text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
text = tokenizer.decode(out[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
if verbose:
print(f" model: {text[:120]}")
word, how = parse_guess(text)
how_tally[how] += 1
turns += 1
try:
feedback = env.guess(f"[{word}]")
except Exception as exc: # env rejected the move, or the game already ended
if verbose:
print(f" env error: {exc}")
break
invalid += int(env.invalid)
if verbose:
print(f" guess=[{word}] invalid={env.invalid} reward={env.reward}")
messages.append({"role": "assistant", "content": text})
messages.append({"role": "user", "content": feedback})
reward = float(env.reward or 0.0)
return {
# A solve is the env paying its terminal success reward — NOT merely a
# positive reward, which the env also pays when it ends an episode early
# for repeated invalid moves.
"solved": bool(env.done and reward >= ENV_SOLVED_REWARD),
"reward": reward,
"turns": turns,
"invalid": invalid,
"how": how_tally,
}Reading the numbers
- solve rate — games where the env paid
ENV_SOLVED_REWARD. This is the headline. - mean reward — includes partial credit, so it moves even when nothing is solved.
- invalid moves — turns the env rejected (usually a guess that isn't 5 letters). A high rate on a small policy is normal and is the thing GRPO has to train away first.
- parse modes —
tool_callshould dominate; a largeregex/rawshare means the eval prompt has drifted from the training format.
💡 Expect a near-zero solve rate from a smoke run: five GRPO steps on a 0.6B policy is enough to prove the pipeline, not to learn Wordle. The point of the smoke run is that every cell executes and the numbers are trustworthy — not that they are good.
In [16]python · cell 31
python
# Solve rate of the trained agent over several games.
# One env is opened for the whole eval and reused across games (reset() per game),
# then closed once at the end — avoids tearing down the shared WebSocket mid-loop.
fine_tuned = AutoModelForCausalLM.from_pretrained(GRPO_OUT, dtype="auto", device_map="auto")
ft_tokenizer = AutoTokenizer.from_pretrained(GRPO_OUT)
eval_env = WordleEnv()
games = []
try:
for g in range(N_EVAL_GAMES):
rec = play_one_game(eval_env, fine_tuned, ft_tokenizer, verbose=(g == 0))
games.append(rec)
print(f"game {g + 1}/{N_EVAL_GAMES}: {'SOLVED' if rec['solved'] else 'not solved'} "
f"reward={rec['reward']:.2f} turns={rec['turns']} invalid={rec['invalid']}")
finally:
try:
eval_env.client.close()
except Exception:
pass
solved = sum(g["solved"] for g in games)
turns = sum(g["turns"] for g in games)
invalid = sum(g["invalid"] for g in games)
mean_reward = sum(g["reward"] for g in games) / max(1, len(games))
parse_modes: Counter = sum((g["how"] for g in games), Counter())
print(f"\nsolve rate: {solved}/{len(games)} = {solved / max(1, len(games)):.0%} mean reward={mean_reward:.2f}")
# Invalid-move rate is the honest companion to the solve rate. A small policy often
# fails by breaking the rules (a six-letter guess) rather than by guessing badly, and
# the env ends such an episode early while still paying partial reward — which is
# exactly why `solved` tests for the terminal success reward and not `reward > 0`.
if turns:
print(f"invalid moves: {invalid}/{turns} turns ({invalid / turns:.0%})")
breakdown = " ".join(f"{mode}={n} ({n / turns:.0%})" for mode, n in parse_modes.most_common())
print(f"parse modes over {turns} turns: {breakdown}")Output
Loading weights: 0%| | 0/310 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 310/310 [00:00<00:00, 930.18it/s]
model: <tool_call>
{"name": "guess", "arguments": {"guess": "[apple]"}}
</tool_call>
guess=[apple] invalid=False reward=0.0
model: <tool_call>
{"name": "guess", "arguments": {"guess": "[banana]"}}
</tool_call>
guess=[banana] invalid=True reward=0.0
model: <tool_call>
{"name": "guess", "arguments": {"guess": "[cherry]"}}
</tool_call>
guess=[cherry] invalid=False reward=0.3
game 1/3: not solved reward=0.30 turns=3 invalid=1
game 2/3: not solved reward=0.30 turns=3 invalid=1
game 3/3: not solved reward=0.40 turns=3 invalid=1 solve rate: 0/3 = 0% mean reward=0.33 invalid moves: 3/9 turns (33%) parse modes over 9 turns: tool_call=9 (100%)
Recap
The workflow is the takeaway. Agent RL is too long-running to babysit in an interactive
kernel, so we submitted the notebook itself to HF Jobs and let papermill execute it on
HF's cloud. The pieces that make that practical:
SMOKEgating — every expensive quantity is an environment variable, so the same file is a five-minute proof or a real run with no cell edits.- Measured calibration — the smoke run reports seconds per rollout on the actual GPU, and the full run's size is derived from that instead of assumed.
- Compute auto-detection — VRAM chooses the policy size, so one notebook runs unchanged across flavors.
- Environment-Space ownership — the env is single-session by design, so a training run needs your own duplicate, both to avoid contending with other users and to be able to restart it when a crashed run leaks its session.
- Train/eval prompt parity — the tool schema is derived from the env class by the same rule TRL uses, so evaluation measures the agent rather than a prompt mismatch.
The training payload itself is TRL's official Wordle example (notebook, guide) — that is deliberate: the point here is what wraps around it.
This is the template for any OpenEnv environment: define the env wrapper (tools = public
documented methods), a reward function that reads env.reward, a dummy prompt dataset for the
episode count, and hand the class to GRPOTrainer(environment_factory=...). Swap the env, keep
the spine — and because env_tools() names no tool, evaluation follows the swap for free.
Where to go next: the OpenEnv SFT-warmup tutorial covers warm-starting a policy with teacher rollouts before GRPO, and the end-to-end walkthrough covers the single-turn case end to end.
References
This recipe builds on
- TRL's official Wordle example:
openenv_wordle_grpo.ipynb— the system prompt,WordleEnvwrapper, reward function, GRPO config, and play-the-game eval follow it directly - TRL OpenEnv guide: huggingface.co/docs/trl/en/openenv — the
environment_factoryagent-training path - OpenEnv SFT-warmup tutorial: docs/openenv/tutorials/sft-warmup — warm-starting a policy with teacher rollouts before GRPO
- OpenEnv end-to-end walkthrough: docs/openenv/tutorials/end-to-end-walkthrough — the single-turn collect → train → evaluate pipeline
Papers and Research
- GRPO Algorithm: Group Relative Policy Optimization — the original GRPO paper (DeepSeekMath), introducing value-free, group-relative policy optimization
- TextArena: TextArena: A Framework for Text-based Game Environments — the multi-turn text-game framework the Wordle environment is built on
Libraries and Frameworks
- TRL (Transformers Reinforcement Learning): huggingface/trl · TRL GRPO docs
- OpenEnv: huggingface/OpenEnv — the environment client and async step/reset API · OpenEnv announcement
- TextArena: LeonGuertler/TextArena — the underlying game engine served by the Wordle Space
- Transformers: huggingface/transformers —
>=5.3.0required for the tool-calling / chat-template behavior GRPO's env path depends on - Hugging Face Jobs: running Jobs from the CLI — non-interactive GPU execution on HF infrastructure
- papermill: papermill docs — parameterized, non-interactive notebook execution
- Trackio: gradio-app/trackio — lightweight live training charts
Environments and Models
- Wordle environment Space: openenv/wordle — the hosted multi-turn OpenEnv/TextArena environment this notebook trains against
- Qwen3-0.6B: Qwen/Qwen3-0.6B — default policy on smaller GPUs
- Qwen3-1.7B: Qwen/Qwen3-1.7B — default policy when VRAM allows
Key Concepts
- Non-interactive notebook execution:
papermillruns the notebook as a batch job on HF Jobs, so hours-long agent RL survives a closed laptop and returns an executed notebook as its artifact - Measured calibration over assumed constants: rollout cost is hardware- and model-dependent, so the smoke run measures it and the full run's size is derived rather than guessed
- Multi-turn agentic RL: one rollout spans several tool calls; the environment signals
done(win or 6 guesses exhausted), so the trainer keeps stepping until the episode ends - Value-free RL (GRPO): no separate reward model — the environment's scalar reward is the only signal; rollouts are ranked within a sampled group
- Stateful feedback slicing: the env returns a cumulative transcript, so each turn we diff out only the newly appended feedback the agent should react to
- Train/eval prompt parity: the eval renders the same tool schema training used, derived from the env class by TRL's own discovery rule, so win rate measures the agent and not a format mismatch
- Single-session environments: OpenEnv environments not marked
SUPPORTS_CONCURRENT_SESSIONSare capped at one session and the cap cannot be raised, which is why a training run needs your own Space duplicate