Hao Zeng

On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models

Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a promising way to optimize calibration. However, it relies on on-policy rollouts and is thus constrained by the model's pre-RL confidence distribution, which we term con...