Understanding and Mitigating Premature Confidence for Better LLM Reasoning
A new paper from arXiv (2605.24396) identifies premature confidence—where models commit to an answer early and rationalize it—as a key predictor of flawed reasoning in long chains of thought. To address this, the authors introduce progressive confidence shaping, a reinforcement learning objective that rewards gradual confidence growth and penalizes early commitment. The method requires no external labels or reward models, making it scalable. Experiments across model sizes from 1.5B to 8B parameters show significant gains: on Countdown arithmetic, accuracy improves 3.2x (+42.0 percentage points) and flawed reasoning drops 48pp; on AIME math, Pass@64 improves 6.6pp. The approach also improves performance on ScienceQA and DAPO benchmarks. The paper suggests that confidence dynamics can serve as a cheap, effective signal for improving reasoning quality without costly step-level annotations.
Enables better LLM reasoning without expensive annotations, improving accuracy and reducing logical gaps.