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1fa3c6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | # 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/meta-pytorch/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()
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