Buffer Matters: Unleashing the Power of Off-Policy Reinforcement Learning in Large Language Model Reasoning

• URL: http://arxiv.org/abs/2602.20722v1
• Date: Tue, 24 Feb 2026 09:35:43 GMT
• Title: Buffer Matters: Unleashing the Power of Off-Policy Reinforcement Learning in Large Language Model Reasoning
• Authors: Xu Wan, Yansheng Wang, Wenqi Huang, Mingyang Sun,
• Abstract summary: Batch Adaptation Policy Optimization (BAPO) is an off-policy RLVR framework to improve the data efficiency in large language models post-training.<n>It dynamically selects training batches by re-evaluating historically difficult samples and reusing high-quality ones.<n>BAPO achieves an average 12.5% improvement over GRPO across mathematics, planning, and visual reasoning tasks.
• Score: 12.863583402455008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional on-policy Reinforcement Learning with Verifiable Rewards (RLVR) frameworks suffer from experience waste and reward homogeneity, which directly hinders learning efficiency on difficult samples during large language models post-training. In this paper, we introduce Batch Adaptation Policy Optimization (BAPO), an off-policy RLVR framework to improve the data efficiency in large language models post-training. It dynamically selects training batches by re-evaluating historically difficult samples and reusing high-quality ones, while holding a lower bound guarantee for policy improvement. Extensive experiments further demonstrate that BAPO achieves an average 12.5% improvement over GRPO across mathematics, planning, and visual reasoning tasks. Crucially, BAPO successfully resolves 40.7% of problems that base models consistently fail to solve.

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