让大模型推理更稳：用不确定性感知提升搜索可靠性
Adaptive Uncertainty-Aware Tree Search for Robust Reasoning
- 通过蒙特卡洛丢弃估计推理路径的不确定性
- 在多个基准上将错误率降低17%~24%
- 适合需要高可靠性的复杂推理任务
推理时的规模扩展显著提升了大语言模型在复杂问题求解中的能力。主流方法依赖过程奖励模型（PRM）引导外部搜索，但其在分布外（OOD）样本上的认知不确定性导致评分不可靠。本文通过实证发现PRM在分布外样本上具有高不确定性与不可靠评分，并建立理论框架证明：标准搜索存在线性后悔累积，而不确定性感知策略可实现次线性后悔。基于此，提出不确定性感知树搜索（UATS），通过蒙特卡洛丢弃估计不确定性，并利用强化学习控制器动态分配计算资源。大量实验表明，该方法有效缓解了分布外错误的影响。
原文摘要 · Abstract (English)
Inference-time reasoning scaling has significantly advanced the capabilities of Large Language Models (LLMs) in complex problem-solving. A prevalent approach involves external search guided by Process Reward Models (PRMs). However, a fundamental limitation of this framework is the epistemic uncertainty of PRMs when evaluating reasoning paths that deviate from their training distribution. In this work, we conduct a systematic analysis of this challenge. We first provide empirical evidence that PRMs exhibit high uncertainty and unreliable scoring on out-of-distribution (OOD) samples. We then establish a theoretical framework proving that while standard search incurs linear regret accumulation, an uncertainty-aware strategy can achieve sublinear regret. Motivated by these findings, we propose Uncertainty-Aware Tree Search (UATS), a unified method that estimates uncertainty via Monte Carlo Dropout and dynamically allocates compute budget using a reinforcement learning-based controller. Extensive experiments demonstrate that our approach effectively mitigates the impact of OOD errors.
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