How Uncertainty Estimation Scales with Sampling in Reasoning Models
Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended chain-of-thought reasoning. We study parallel sampling as a fully black-box approach using verbalized confidence and self-consistency. Across three reasoning models and 17 tasks spanning mathematics, STEM, and humanities, we characterize how these signals scale. Both self-consistency and verbalized confidence scale in reasoning models, but self-consistency exhibits lower initial discrimination and lags behind verbalized confidence under moderate sampling. Most uncertainty gains, however, arise from signal combination: with just two samples, a hybrid estimator improves A...
| Authors | Maksym Del, Markus Kängsepp, Marharyta Domnich et al. |
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| Primary category | cs.AI |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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