RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning

• URL: http://arxiv.org/abs/2507.07451v1
• Date: Thu, 10 Jul 2025 05:58:55 GMT
• Title: RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
• Authors: Hongzhi Zhang, Jia Fu, Jingyuan Zhang, Kai Fu, Qi Wang, Fuzheng Zhang, Guorui Zhou,
• Abstract summary: Reinforcement learning (RL) for large language models is an energy-intensive endeavor.<n>We present emphRLEP, a framework that first collects verified trajectories and then replays them during subsequent training.<n>At every update step, the policy is optimized on mini-batches that blend newly generated rollouts with replayed successes.
• Score: 18.62575670251997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) for large language models is an energy-intensive endeavor: training can be unstable, and the policy may gradually drift away from its pretrained weights. We present \emph{RLEP}\, -- \,Reinforcement Learning with Experience rePlay\, -- \,a two-phase framework that first collects verified trajectories and then replays them during subsequent training. At every update step, the policy is optimized on mini-batches that blend newly generated rollouts with these replayed successes. By replaying high-quality examples, RLEP steers the model away from fruitless exploration, focuses learning on promising reasoning paths, and delivers both faster convergence and stronger final performance. On the Qwen2.5-Math-7B base model, RLEP reaches baseline peak accuracy with substantially fewer updates and ultimately surpasses it, improving accuracy on AIME-2024 from 38.2% to 39.9%, on AIME-2025 from 19.8% to 22.3%, and on AMC-2023 from 77.0% to 82.2%. Our code, datasets, and checkpoints are publicly available at https://github.com/Kwai-Klear/RLEP to facilitate reproducibility and further research.

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