A Theoretical Study on Bridging Internal Probability and
  Self-Consistency for LLM Reasoning
Authors: name: Zhi Zhou; name: Yuhao Tan; name: Zenan Li; name: Yuan Yao; name: Lan-Zhe Guo; name: Yu-Feng Li; name: Xiaoxing Ma
Published: 2025-10-17 08:59:30

Abstract: Test-time scaling seeks to improve the reasoning performance of large
language models (LLMs) by adding computational resources. A prevalent approach
within the field is sampling-based test-time scaling methods, which enhance
reasoning by generating multiple reasoning paths for a given input during
inference. However, despite its practical success, the theoretical foundations
remain underexplored. In this paper, we provide the first theoretical framework
for analyzing sampling-based test-time scaling methods, grounded in the
perspective of confidence estimation. Based on the framework, we analyze two
dominant paradigms: self-consistency and perplexity, and reveal key
limitations: self-consistency suffers from high estimation error while
perplexity exhibits substantial modeling error and possible degradation of the
estimation error convergence. To address these limitations, we introduce RPC, a
hybrid method that leverages our theoretical insights through two key
components: Perplexity Consistency and Reasoning Pruning. Perplexity
Consistency combines the strengths of self-consistency and perplexity, boosting
the convergence rate of estimation error from linear to exponential while
preserving model error. Reasoning Pruning prevents degradation by eliminating
low-probability reasoning paths. Both theoretical analysis and empirical
results across seven benchmark datasets demonstrate that RPC has a strong
potential for reducing reasoning error. Notably, RPC achieves reasoning
performance comparable to self-consistency while not only enhancing confidence
reliability but also reducing sampling costs by 50%. The code and resources are
available at https://wnjxyk.github.io/RPC.