Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps
  via Uncertainty Heads
Authors: name: Jingwei Ni, name: Ekaterina Fadeeva, name: Tianyi Wu, name: Mubashara Akhtar, name: Jiaheng Zhang, name: Elliott Ash, name: Markus Leippold, name: Timothy Baldwin, name: See-Kiong Ng, name: Artem Shelmanov, name: Mrinmaya Sachan

Solving complex tasks usually requires LLMs to generate long multi-step
reasoning chains. Previous work has shown that verifying the correctness of
individual reasoning steps can further improve the performance and efficiency
of LLMs on such tasks and enhance solution interpretability. However, existing
verification approaches, such as Process Reward Models (PRMs), are either
computationally expensive, limited to specific domains, or require large-scale
human or model-generated annotations. Thus, we propose a lightweight
alternative for step-level reasoning verification based on data-driven
uncertainty scores. We train transformer-based uncertainty quantification heads
(UHeads) that use the internal states of a frozen LLM to estimate the
uncertainty of its reasoning steps during generation. The approach is fully
automatic: target labels are generated either by another larger LLM (e.g.,
DeepSeek R1) or in a self-supervised manner by the original model itself.
UHeads are both effective and lightweight, containing less than 10M parameters.
Across multiple domains, including mathematics, planning, and general knowledge
question answering, they match or even surpass the performance of PRMs that are
up to 810x larger. Our findings suggest that the internal states of LLMs encode
their uncertainty and can serve as reliable signals for reasoning verification,
offering a promising direction toward scalable and generalizable introspective
LLMs.