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

2026-07-20 20:54:03

AIMPACT message, on April 26 (UTC+8), MIT CSAIL researchers found that top reasoning models became overly confident during reinforcement learning training because they only rewarded correct answers and did not account for confidence. The research team proposed a new method to train the model to estimate the confidence of each answer, significantly improving its uncertainty estimation capabilities without harming accuracy. The work reveals a key flaw in current reinforcement learning training mechanisms and proposes a viable direction for improvement. (Source: InFoQ)

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