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MIT CSAIL: Rewarding only correct answers causes reasoning models to be overconfident; a new method improves uncertainty estimation

MeNews
AIMPACT update, April 26 (UTC+8): MIT CSAIL researchers found that top reasoning models become overconfident during reinforcement learning training because they only reward correct answers without considering confidence. The research team proposed a new method to train the model to estimate the confidence for each answer, significantly improving its uncertainty estimation ability without harming accuracy. The work highlights a key flaw in current reinforcement learning training mechanisms and proposes an effective direction for improvement. (Source: InFoQ)
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