English(EN) Clip-Low Increases Entropy and Clip-High Decreases Entropy in Reinforcement Learning of Large Language Models

研究论文详细介绍了 LLM 强化学习中的裁剪如何影响熵

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新的研究论文探讨了裁剪机制对大型语言模型（LLM）强化学习的影响。研究表明，“clip-low”会增加熵，促进探索，而“clip-high”会降低熵，可能导致熵崩溃。论文证明，“clip-high”效应通常占主导地位，即使在随机奖励的情况下也会降低熵，并提出通过有意识地调整裁剪参数可以控制熵并防止训练过程中的崩溃。 AI

影响这项研究可能带来更稳定、更有效的 LLM 训练方法，从而提高它们的推理能力。

排序理由该集群包含一篇学术论文，详细介绍了 LLM 强化学习方面的新发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jaesung R. Park, Junsu Kim, Gyeongman Kim, Jinyoung Jo, Sean Choi, Jaewoong Cho, Ernest K. Ryu · 2026-10-27 04:00

Clip-Low 增加大语言模型强化学习中的熵，Clip-High 降低熵

arXiv:2509.26114v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently emerged as the leading approach for enhancing the reasoning capabilities of large language models (LLMs). However, RLVR is prone to entropy collapse, where the L…

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Clip-Low 增加大语言模型强化学习中的熵，Clip-High 降低熵

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