重新思考推理路径为相位结构轨迹
Rethinking Reasoning Paths as Phase-Structured Trajectories
- University of Virginia（弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将推理路径视为相位结构轨迹，通过PAIR方法在问题内对齐相位以分离路径质量信号，提升轨迹排序与选择性能。
AI中文摘要：
大型语言模型通常通过生成多步推理路径来提高问题解决性能，但如何分析这些路径上的隐藏状态仍不清楚。现有方法通常为每个中间状态分配最终答案正确性标签，并在异质问题上训练探针。我们认为这一协议在两个方面掩盖了推理动态：（1）正确性预测可以利用问题层面的变化而非路径质量，（2）按绝对步骤索引对齐的状态可能对应推理的不同功能相位。在这项工作中，我们提出将推理路径视为固定问题内的相位结构轨迹。我们将这一观点实例化为PAIR，即相位对齐的问题内推理。PAIR为每个问题采样多条轨迹，基于归一化轨迹进度将可变长度路径映射到共享的相对相位，并且仅在同一问题和相位内比较成功与不成功的轨迹。这产生了相位特定的路径质量方向，更好地从问题层面变化中分离出路径质量信号。实验上，我们发现标准的跨问题正确性探针在问题内评估下失去大部分预测能力，表明这些探针部分依赖于问题层面信息。PAIR在跨模型和基准的问题内轨迹排序和Best-of-N轨迹选择上有所改进。相位级引导进一步表明，学习到的方向可以改变生成结果，提供因果证据表明它们捕获了轨迹相关信息。
英文摘要：
Large language models often improve problem-solving performance by generating multi-step reasoning paths, yet how to analyze the hidden states along these paths remains unclear. Existing approaches typically assign each intermediate state the final-answer correctness label and train probes across heterogeneous questions. We argue that this protocol obscures reasoning dynamics in two ways: (1) correctness prediction can exploit question-level variation rather than path quality, and (2) states aligned by absolute step indices may correspond to different functional phases of reasoning. In this work, we propose to view reasoning paths as phase-structured trajectories within fixed questions. We instantiate this view as PAIR, short for Phase-Aligned Intra-question Reasoning. PAIR samples multiple trajectories for each question, maps variable-length paths into shared relative phases based on normalized trajectory progress, and compares successful and unsuccessful trajectories only within the same question and phase. This yields phase-specific path-quality directions that better isolate path-quality signals from question-level variation. Empirically, we find that standard across-question correctness probes lose much of their predictive power under within-question evaluation, suggesting that these probes partly rely on question-level information. PAIR improves within-question trajectory ranking and Best-of-N trajectory selection across models and benchmarks. Phase-wise steering further shows that the learned directions can change generation outcomes, providing causal evidence that they capture trajectory-relevant information.
