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

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cs.LG2025★ 1 cited

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

Jaesung R. Park, Junsu Kim, Gyeongman Kim +4

Reinforcement learning with verifiable rewards (RLVR) has recently emerged as the leading approach for enhancing the reasoning capabilities of large language models (LLMs). However…

cs.LG2024

Task Diversity Shortens the ICL Plateau

Jaeyeon Kim, Sehyun Kwon, Joo Young Choi +4

In-context learning (ICL) describes a language model's ability to generate outputs based on a set of input demonstrations and a subsequent query. To understand this remarkable capa…