How Off-Policy Can GRPO Be? Mu-GRPO for Efficient LLM Reinforcement Learning

• URL: http://arxiv.org/abs/2605.17570v1
• Date: Sun, 17 May 2026 17:58:53 GMT
• Title: How Off-Policy Can GRPO Be? Mu-GRPO for Efficient LLM Reinforcement Learning
• Abstract summary: We show that GRPO-style algorithms can tolerate substantially larger rollout staleness than previously assumed.<n>We propose Mu-GRPO, an RL training framework that organizes training into a small number of sequential generation-optimization stages.<n>Across five language models and multiple math reasoning benchmarks, Mu-GRPO matches or exceeds the performance of standard GRPO.
• Score: 7.042913010118603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group Relative Policy Optimization (GRPO) has been a key driver of recent progress in reinforcement learning with verifiable rewards (RLVR) for large language models, but it is typically trained in a low-staleness, near-on-policy regime that incurs substantial system overhead. We ask a simple question: How off-policy can GRPO be? We show that GRPO-style algorithms can tolerate substantially larger rollout staleness than previously assumed, and propose Mu-GRPO, an RL training framework that organizes training into a small number (e.g., four) of large sequential generation-optimization stages. This design induces high rollout staleness while greatly reducing rollout-optimization switching overhead. To stabilize learning under stale data, Mu-GRPO combines relaxed clipping, which preserves useful stale-rollout gradients, with negative-advantage veto, which removes destabilizing post-trigger suffix updates in negative-advantage responses. Across five language models and multiple math reasoning benchmarks, Mu-GRPO matches or exceeds the performance of standard GRPO while achieving around 2x speedup in wall-clock training time, establishing a substantially improved performance-efficiency trade-off for LLM reinforcement learning.

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