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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 distributio
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