Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs 事件

Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs arXiv:2605.28388v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) is empirically shown to notably enhance the reasoning performance of large language models (LLMs), particularly in mathematics and programming. However, the mechanistic role of Sample Difficulty in RLVR remains poorly understood. In this paper, we investigate RLVR through the lens of difficulty-wise and one-sample analys