研究推理模型不确定性估计在采样增多时的表现，发现组合信号效果更好。
How Uncertainty Estimation Scales with Sampling in Reasoning Models
- 用口头置信度和自洽性做黑箱不确定性估计
- 两样本组合即提升效果，大幅优于单一信号
- 数学任务中效果更优，且信号互补性强
不确定性估计对部署推理语言模型至关重要，但在扩展链式思维推理中仍理解不足。本文通过口头置信度与自洽性，研究并行采样在三种推理模型、17个跨数学、STEM和人文学科任务中的表现。结果表明，自洽性和口头置信度均随采样增长而提升，但自洽性初始区分度较低且在中等采样下落后于口头置信度。大部分不确定性增益来自信号组合：仅用两个样本，混合估计算法平均提升AUROC达+12，已超越任一单独信号，即使扩大采样预算后收益也趋于饱和。该效应具有领域依赖性：在数学任务中，基于RLVR风格后训练的模型具备更高不确定性质量，表现出更强互补性与更快的缩放速度。
原文摘要 · Abstract (English)
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 AUROC by up to $+12$ on average and already outperforms either signal alone even when scaled to much larger budgets, after which returns diminish. These effects are domain-dependent: in mathematics, the native domain of RLVR-style post-training, reasoning models achieve higher uncertainty quality and exhibit both stronger complementarity and faster scaling than in STEM or humanities.
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