OPA：通过提纲引导的路径探索克服并行思维中的信息过载

OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration

February 9, 2026

作者: Qi Guo, Jianing Wang, Deyang Kong, Xiangyu Xi, Jianfei Zhang, Yi Lu, Jingang Wang, Wei Wang, Shikun Zhang, Wei Ye

cs.AI

摘要

并行思维已成为大型推理模型处理复杂问题的新范式。近期研究通过强化学习增强并行思维，旨在解决监督微调在计算资源和效果上的局限性。然而现有方法主要聚焦于答案聚合阶段的优化，对路径探索环节的关注不足。本文从可验证奖励的强化学习理论框架出发，通过理论分析发现探索路径间的互信息瓶颈是制约并行思维性能的根本因素。为此，我们提出大纲引导的路径探索方法，通过在并行路径推理前生成多样化推理大纲来显式划分解空间，从而降低信息冗余并提升路径间信息捕获的多样性。我们采用迭代式强化学习策略独立优化大纲规划与大纲引导推理，在多个高难度数学推理基准上的实验表明，该方法能有效提升不同聚合策略下的推理性能，使大型推理模型更可靠地发现正确解。

English

Parallel thinking has emerged as a new paradigm for large reasoning models (LRMs) in tackling complex problems. Recent methods leverage Reinforcement Learning (RL) to enhance parallel thinking, aiming to address the limitations in computational resources and effectiveness encountered with supervised fine-tuning. However, most existing studies primarily focus on optimizing the aggregation phase, with limited attention to the path exploration stage. In this paper, we theoretically analyze the optimization of parallel thinking under the Reinforcement Learning with Verifiable Rewards (RLVR) setting, and identify that the mutual information bottleneck among exploration paths fundamentally restricts overall performance. To address this, we propose Outline-Guided Path Exploration (OPE), which explicitly partitions the solution space by generating diverse reasoning outlines prior to parallel path reasoning, thereby reducing information redundancy and improving the diversity of information captured across exploration paths. We implement OPE with an iterative RL strategy that optimizes outline planning and outline-guided reasoning independently. Extensive experiments across multiple challenging mathematical benchmarks demonstrate that OPE effectively improves reasoning performance in different aggregation strategies, enabling LRMs to more reliably discover correct solutions.