Efficient RL Training for LLMs with Experience Replay

arXiv:2604.08706v1 Announce Type: new
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.