Align GRPO and RLOO initialization #4685
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qgallouedec
commented
Dec 12, 2025
Comment on lines
120
to
131
| from datasets import load_dataset | ||
| from trl import RLOOTrainer | ||
| from trl.rewards import accuracy_reward | ||
| dataset = load_dataset("trl-lib/tldr", split="train") | ||
| def reward_func(completions, **kwargs): | ||
| # Dummy reward function that rewards completions with more unique letters. | ||
| return [float(len(set(completion))) for completion in completions] | ||
| dataset = load_dataset("trl-lib/DeepMath-103K", split="train") | ||
| trainer = RLOOTrainer( | ||
| model="Qwen/Qwen2-0.5B-Instruct", | ||
| reward_funcs=reward_func, | ||
| reward_funcs=accuracy_reward, | ||
| train_dataset=dataset, | ||
| ) | ||
| trainer.train() |
| model_name = model_name.split("/")[-1] | ||
| args = RLOOConfig(f"{model_name}-RLOO") | ||
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| # Models |
qgallouedec
requested review from
albertvillanova,
edbeeching,
kashif and
lewtun
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GRPO recently benefited from some improvements in initialization that were not applied to RLOO. This PR aligns the two initializations.