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Research paper details how LLM reinforcement learning clipping affects entropy

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the impact of clipping mechanisms in reinforcement learning for large language models (LLMs). The study reveals that 'clip-low' increases entropy, promoting exploration, while 'clip-high' decreases it, potentially leading to entropy collapse. The paper demonstrates that the 'clip-high' effect often dominates, reducing entropy even with random rewards, and suggests that deliberately adjusting clipping parameters can control entropy and prevent collapse during training. AI

IMPACT This research could lead to more stable and effective training methods for LLMs, improving their reasoning capabilities.

RANK_REASON The cluster contains an academic paper detailing novel findings in reinforcement learning for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research paper details how LLM reinforcement learning clipping affects entropy

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The cluster contains an academic paper detailing novel findings in reinforcement learning for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jaesung R. Park, Junsu Kim, Gyeongman Kim, Jinyoung Jo, Sean Choi, Jaewoong Cho, Ernest K. Ryu ·

    Clip-Low Increases Entropy and Clip-High Decreases Entropy in Reinforcement Learning of Large Language Models

    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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