Calibration, Not Answer Selection: Distilling Internal Confidence in Reasoning Models
Reinforcement learning with binary correctness rewards trains correctness, not calibrated confidence. The confidence that reasoning models verbalize is systematically overconfident, and the problem is not merely one of scale: verbalized confidence tracks how willing a model is to commit to an answer, not how likely the answer is to be right. Post-hoc rescaling therefore fits one distribution but rarely transfers. We look inside the model instead. On factual question answering, a linear probe on the hidden state between the chain of thought and the answer is substantially better calibrated: its expected calibration error is 5 to 38 times lower than that of the verbalized score across four ...
| Authors | Yadong Xi, Rongsheng Zhang, Tangjie Lv et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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