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MIT CSAIL: Rewarding only correct answers makes reasoning models overly confident; a new method improves uncertainty estimation

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AIMPACT message, April 26 (UTC+8): MIT CSAIL research found that top reasoning models become overconfident during reinforcement learning training because they only reward correct answers without taking confidence into account. The research team proposed a new method: training the model to estimate the confidence level for each answer, significantly improving uncertainty estimation capabilities without harming accuracy. The work exposes a key flaw in current reinforcement learning training mechanisms and suggests an effective direction for improvement. (Source: InFoQ)
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