Understanding and Mitigating Premature Confidence for Better LLM Reasoning
The paper discusses the issue of premature confidence in language models, which leads to flawed reasoning. It introduces a method called progressive confidence shaping that encourages models to update their confidence gradually during reasoning. This approach has shown significant improvements in accuracy and reasoning quality across various tasks and model sizes.
- ▪Premature confidence in language models often results in logical gaps and flawed reasoning.
- ▪The proposed method, progressive confidence shaping, trains models to adjust their confidence as they reason.
- ▪Improvements in accuracy and reasoning quality were observed across different tasks, with notable gains in models ranging from 1.5B to 8B parameters.
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Computer Science > Artificial Intelligence arXiv:2605.24396 (cs) [Submitted on 23 May 2026] Title:Understanding and Mitigating Premature Confidence for Better LLM Reasoning Authors:Jingchu Gai, Guanning Zeng, Christina Baek, Chen Wu, J.Zico Kolter, Andrej Risteski, Aditi Raghunathan View a PDF of the paper titled Understanding and Mitigating Premature Confidence for Better LLM Reasoning, by Jingchu Gai and 6 other authors View PDF HTML (experimental) Abstract:Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps, limiting the gains from additional test-time compute. Improving reasoning quality directly would require process reward models, but the step-level annotations needed to train them are expensive and scarce.
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