English(EN) On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

新方法解决LLM强化学习中的训练-推理不匹配问题

作者 PulseAugur 编辑部 · [2 个来源] · 2026-10-29 00:00

研究人员识别并解决了大型语言模型强化学习中的一个关键问题，即训练-推理不匹配。这种不匹配源于推理引擎中的数值差异和异步采样中的权重陈旧，导致策略外训练数据。该研究提出了一种新颖的“差异滞后校正”（DLC）方法，通过中心化数值误差、裁剪重要性比率和重新同步采样器来稳定训练。在Qwen3模型上进行测试时，DLC与现有方法相比显著提高了训练稳定性，将KL散度保持在可接受范围内，并防止了训练崩溃。 AI

影响引入了一种新颖的校正方法，以提高LLM训练的稳定性和效率。

排序理由该集群包含一篇详细介绍LLM强化学习新方法的学术论文。

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Newsworthiness bucket: Research; 该集群包含一篇详细介绍LLM强化学习新方法的学术论文。
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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Yu Li, Yufa Zhou, Tian Lan · 2026-11-02 05:00

On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

arXiv:2610.40945v1 Announce Type: new Abstract: On-policy reinforcement learning for large language models assumes that training responses are sampled from the current policy. In practice, an inference engine samples the responses and a separate trainer computes the gradients, an…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-29 00:00

On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

On-policy reinforcement learning for large language models suffers from training-inference mismatch due to numerical discrepancies and policy lag, and a derived discrepancy-lag correction stabilizes training by centering errors, clipping importance ratios, and resynchronizing sam…

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报道来源 [2]

On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

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