用前缀重要性比率稳定 off-policy LLM 优化：MinPRO 方法

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Abstract:Reinforcement learning (RL) post-training has increasingly demonstrated strong ability to elicit reasoning behaviors in large language models (LLMs). For training efficiency, rollouts are typically generated in an off-policy manner using an older sampling policy and then used to update the current target policy. To correct the resulting discrepancy between the sampling and target policies, most existing RL objectives rely on a token-level importance sampling ratio. However, we observe unstable training dynamics in existing importance-weighted objectives under large policy lag. In this paper, we revisit LLM policy optimization under off-policy conditions and show that the exact correction for the prefix-marginal policy gradient is the cumulative prefix importance ratio. Token-level weighting does not explicitly correct for this prefix distribution shift. To stabilize LLM optimization under large off-policy drift, we propose a simple yet effective objective, Minimum Prefix Ratio (MinPRO). MinPRO replaces the unstable cumulative prefix ratio with a biased, non-cumulative surrogate based on the minimum token-level ratio observed in the preceding prefix. Extensive experiments on both dense and mixture-of-experts LLMs, across multiple mathematical reasoning benchmarks, demonstrate that MinPRO substantially improves training stability and peak performance in off-policy regimes.

Submission history

From: Shiye Lei [view email]
[v1] Fri, 30 Jan 2026 08:47:19 UTC (308 KB)
[v2] Wed, 4 Nov 2026 06:52:42 UTC (246 KB)