Efficient RL Training for LLMs with Experience Replay

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

Efficient RL Training for LLMs with Experience Replay: 24 upvotes on Hugging Face Daily Papers, #16 of 48 papers on 2026-04-14. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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