AINeutralarXiv – CS AI · 3h ago6/10
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Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs
Researchers mechanistically analyze how sample difficulty affects Reinforcement Learning with Verifiable Reward (RLVR) training in large language models, discovering that medium-difficulty problems yield optimal reasoning improvements while overly hard problems degrade performance. The study proposes difficulty-adaptive strategies using backward-reasoning reformulation and sparse autoencoders to optimize reward signals during training.