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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "trl",
# "trackio",
# "openenv-textarena @ git+https://huggingface.co/spaces/openenv/wordle",
# ]
# ///
"""
Simple script to run GRPO training with OpenEnv's Wordle environment and vLLM.
Setup (Option A - Install from HF Space, recommended):
```sh
uv pip install git+https://huggingface.co/spaces/openenv/wordle
```
# Option 1: HF Spaces + Colocated vLLM (1 GPU required)
```sh
python examples/scripts/openenv/wordle.py --vllm-mode colocate
```
# Option 2: HF Spaces + Separate vLLM server (2 GPUs required)
# Spin up vLLM server (Terminal 1)
```sh
CUDA_VISIBLE_DEVICES=0 trl vllm-serve --model Qwen/Qwen3-1.7B --host 0.0.0.0 --port 8000
```
# Run training (Terminal 2)
```sh
CUDA_VISIBLE_DEVICES=1 python examples/scripts/openenv/wordle.py --vllm-mode server --vllm-server-url http://localhost:8000
```
# Option 3: Local Environment + Colocated vLLM (1 GPU required)
To run the Wordle environment locally, you have several options:
## Option 3a: Using Docker Image (Recommended)
First, build the Docker image from the textarena_env directory:
```sh
cd 3rd_party/OpenEnv/envs/textarena_env
docker build -t textarena-env:latest -f server/Dockerfile .
```
Then run the environment server:
```sh
docker run -d -p 8001:8001 textarena-env:latest
```
Finally, run training pointing to local server:
```sh
python examples/scripts/openenv/wordle.py --vllm-mode colocate --env-url http://localhost:8001
```
## Option 3b: Running Server Directly
From the textarena_env directory:
```sh
cd 3rd_party/OpenEnv/envs/textarena_env
uv venv && source .venv/bin/activate
uv pip install -e .
python -m uvicorn server.app:app --reload --port 8001
```
Then in another terminal, run training:
```sh
python examples/scripts/openenv/wordle.py --vllm-mode colocate --env-url http://localhost:8001
```
## Option 3c: Using Pre-built HF Space Image
```sh
docker run -d -p 8001:8001 registry.hf.space/burtenshaw-wordle:latest
python examples/scripts/openenv/wordle.py --vllm-mode colocate --env-url http://localhost:8001
```
"""
import argparse
from datasets import Dataset
from textarena_env import TextArenaAction, TextArenaEnv
from trl import GRPOConfig, GRPOTrainer, RichProgressCallback
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Run GRPO training for Wordle using the TextArena OpenEnv environment."
)
parser.add_argument(
"--model",
type=str,
default="Qwen/Qwen3-1.7B",
help="Model identifier passed to GRPOTrainer for fine-tuning.",
)
parser.add_argument(
"--env-url",
type=str,
default="https://openenv-wordle.hf.space",
help="URL for the environment server.",
)
parser.add_argument(
"--dataset-size",
type=int,
default=1000,
help="Number of entries to include in the synthetic training dataset.",
)
parser.add_argument(
"--num-generations",
type=int,
default=4,
help="Number of rollout generations per dataset prompt.",
)
parser.add_argument(
"--num-epochs",
type=int,
default=1,
help="Number of training epochs.",
)
parser.add_argument(
"--learning-rate",
type=float,
default=1e-6,
help="Learning rate for GRPO training.",
)
parser.add_argument(
"--gradient-accumulation-steps",
type=int,
default=64,
help="Gradient accumulation steps for GRPO training.",
)
parser.add_argument(
"--logging-steps",
type=int,
default=1,
help="Frequency of logging steps for GRPO training.",
)
parser.add_argument(
"--output-dir",
type=str,
default=None,
help="Directory where training outputs and checkpoints are stored.",
)
parser.add_argument(
"--trackio-space-id",
type=str,
default="wordle-grpo",
help="Trackio space identifier.",
)
parser.add_argument(
"--vllm-mode",
choices=("colocate", "server"),
default="colocate",
help="vLLM execution mode: 'colocate' or 'server'.",
)
parser.add_argument(
"--vllm-server-url",
type=str,
default="http://localhost:8000",
help="URL for the vLLM server (only used when --vllm-mode=server).",
)
return parser.parse_args()
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.
"""
def reward_func(environments, **kwargs) -> list[float]:
return [env.reward for env in environments]
def main() -> None:
args = parse_args()
env_url = args.env_url
class WordleEnv:
def __init__(self):
self.client = TextArenaEnv(base_url=env_url)
def reset(self, **kwargs) -> str | None:
result = self.client.reset()
# The game returns cumulative feedback each turn (new text appended at the end), so
# we store the previous full response and slice out only the newly appended part.
self._last_full_feedback = result.observation.messages[0].content
self.reward = 0.0
self.done = 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_feedback = result.observation.messages[0].content
# Just take the new feedback since the last guess, which is the part appended to the end of the full feedback
feedback = _full_feedback[len(self._last_full_feedback) :]
self._last_full_feedback = _full_feedback
# For some reason, the environment doesn't penalize invalid moves and just returns the last reward.
# We check the feedback for the invalid move message and penalize it if found.
if "You attempted an invalid move" in feedback:
self.reward = 0.0
else:
self.reward = result.reward
self.done = result.done
return feedback
output_dir = args.output_dir or f"{args.model.split('/')[-1]}-wordle-GRPO"
dataset = Dataset.from_dict({"prompt": [[{"role": "user", "content": prompt}] for _ in range(args.dataset_size)]})
trainer = GRPOTrainer(
model=args.model,
reward_funcs=reward_func,
train_dataset=dataset,
args=GRPOConfig(
output_dir=output_dir,
use_vllm=True,
vllm_mode=args.vllm_mode,
vllm_server_base_url=args.vllm_server_url if args.vllm_mode == "server" else None,
report_to="trackio",
trackio_space_id=args.trackio_space_id,
log_completions=True,
num_completions_to_print=2,
logging_steps=args.logging_steps,
num_train_epochs=args.num_epochs,
num_generations=args.num_generations,
learning_rate=args.learning_rate,
gradient_accumulation_steps=args.gradient_accumulation_steps,
chat_template_kwargs={"enable_thinking": False},
max_completion_length=1024,
),
environment_factory=WordleEnv,
callbacks=[RichProgressCallback()],
)
trainer.train()
if __name__ == "__main__":
main()
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