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

collaborators

1 paper

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…