Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs

ArXi:2605.28388v1 Announce Type: new 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 analysis.