AI RESEARCH
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
arXiv CS.LG
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ArXi:2604.23333v1 Announce Type: new Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability. Yet, outcome-based reward often incentivizes models to be overconfident, leading to hallucinations, unreliable confidence-based control, and unnecessary compute allocation. We