English(EN) Stabilizing Off-policy LLM Optimization with Prefix Importance Ratio

新的MinPRO目标稳定离策略LLM强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一种名为最小前缀比率（MinPRO）的新目标函数，用于稳定大型语言模型（LLM）的离策略强化学习。现有使用token级别重要性比率的方法在采样策略和目标策略之间存在显著延迟时，在训练稳定性方面存在困难。MinPRO通过使用基于前缀内最小token级别比率的代理来解决这个问题，证明了其在密集和混合专家LLM的数学推理基准测试中具有更高的稳定性和性能。 AI

影响这项研究可能带来更稳定、更高效的LLM训练，以应对复杂的推理任务。

排序理由该集群包含一篇详细介绍LLM优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍LLM优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Shiye Lei, Zhihao Cheng, Dacheng Tao · 2026-11-05 05:00

使用前缀重要性比率稳定离策略LLM优化

arXiv:2601.22718v2 Announce Type: replace 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 man…

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报道来源 [1]

使用前缀重要性比率稳定离策略LLM优化

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