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# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# 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",
# "openenv-echo-env @ git+https://huggingface.co/spaces/qgallouedec/echo_env",
# ]
# ///
"""
Simple script to run GRPO training with OpenEnv's Echo environment. The environment echoes back the message
sent to it and rewards longer completions.
Setup (Option A - Install from HF Space, recommended):
```sh
uv pip install git+https://huggingface.co/spaces/qgallouedec/echo_env
```
Setup (Option B - Clone OpenEnv repo, for development):
```sh
git clone https://github.com/huggingface/OpenEnv.git
cd OpenEnv/envs/echo_env
uv pip install -e .
```
Usage:
```sh
python examples/scripts/openenv/echo.py
python examples/scripts/openenv/echo.py --model Qwen/Qwen2.5-0.5B-Instruct --env-host https://qgallouedec-echo-env.hf.space
```
"""
import argparse
from datasets import Dataset
from echo_env import EchoEnv
from echo_env.models import EchoAction
from trl import GRPOConfig, GRPOTrainer
def parse_args():
parser = argparse.ArgumentParser(description="Run GRPO training with Echo environment.")
parser.add_argument(
"--model",
type=str,
default="Qwen/Qwen3-0.6B",
help="Model to use for training.",
)
parser.add_argument(
"--env-host",
type=str,
default="https://qgallouedec-echo-env.hf.space",
help="URL for the Echo environment HF Space.",
)
return parser.parse_args()
def reward_func(environments, **kwargs):
return [env.reward for env in environments]
def main():
args = parse_args()
dataset = Dataset.from_dict(
{
"prompt": [
[{"role": "user", "content": "Try to echo 'Hello World!' in the environment."}],
[{"role": "user", "content": "Make the environment echo 'Goodbye World!'"}],
[{"role": "user", "content": "Can you ask the environment to echo 'TRL is great!'?"}],
[{"role": "user", "content": "What happens if you ask the environment to echo 'I love RLHF!'?"}],
[{"role": "user", "content": "Try to make the environment echo 'OpenEnv is awesome!'"}],
],
}
)
class EchoToolEnv:
def __init__(self):
self.env = EchoEnv(base_url=args.env_host)
self.reward = 0.0
def reset(self, **kwargs) -> None | str:
self.reward = 0.0
return None
def echo(self, message: str) -> str:
"""
Echo the message back from the environment.
Args:
message: The message to echo
Returns:
The echoed message.
"""
observation = self.env.step(EchoAction(message=message))
self.reward = observation.observation.reward
return observation.observation.echoed_message
trainer = GRPOTrainer(
model=args.model,
train_dataset=dataset,
reward_funcs=reward_func,
args=GRPOConfig(
chat_template_kwargs={"enable_thinking": False},
log_completions=True,
logging_steps=2,
num_completions_to_print=1,
),
environment_factory=EchoToolEnv,
)
trainer.train()
if __name__ == "__main__":
main()