Efficient RL Training for LLMs with Experience Replay

Charles Arnal; Vivien Cabannes; Taco Cohen; Julia Kempe; Remi Munos

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arXiv · 2604.08706

Efficient RL Training for LLMs with Experience Replay

Charles Arnal·Vivien Cabannes·Taco Cohen·Julia Kempe·Remi Munos

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Abstract

While Experience Replay - the practice of storing rollouts and reusing them multiple times during training - is a foundational technique in general RL, it remains largely unexplored in LLM post-training due to the prevailing belief that fresh, on-policy data is essential for high performance. In this work, we challenge this assumption. We present a systematic study of replay buffers for LLM post-training, formalizing the optimal design as a trade-off between staleness-induced variance, sample diversity and the high computational cost of generation. We show that strict on-policy sampling is suboptimal when generation is expensive. Empirically, we show that a well-designed replay buffer can drastically reduce inference compute without degrading - and in some cases even improving - final model performance, while preserving policy entropy.

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Charles Arnal, Vivien Cabannes, Taco Cohen, Julia Kempe, Remi Munos. 2026-04-09. Efficient RL Training for LLMs with Experience Replay. https://arxiv.org/abs/2604.08706

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