Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training

• URL: http://arxiv.org/abs/2505.22257v2
• Date: Fri, 30 May 2025 03:55:41 GMT
• Title: Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
• Abstract summary: We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes.<n>We show that both on-policy and off-policy GRPO objectives yield an improvement in the reward.<n>We then compare the empirical performance of reinforcement learning with verifiable rewards in post-training using both GRPO variants.
• Score: 19.783852824564736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes. Our motivation comes from recent work on off-policy Proximal Policy Optimization (PPO), which improves training stability, sampling efficiency, and memory usage. In addition, a recent analysis of GRPO suggests that estimating the advantage function with off-policy samples could be beneficial. Building on these observations, we adapt GRPO to the off-policy setting. We show that both on-policy and off-policy GRPO objectives yield an improvement in the reward. This result motivates the use of clipped surrogate objectives in the off-policy version of GRPO. We then compare the empirical performance of reinforcement learning with verifiable rewards in post-training using both GRPO variants. Our results show that off-policy GRPO either significantly outperforms or performs on par with its on-policy counterpart.

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