OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration
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.
698bf2606052d3bed9630a57
Qi Guo
698bf2606052d3bed9630a58
Jianing Wang
698bf2606052d3bed9630a59
Deyang Kong
claimed_verified
2026-02-11T11:14:45.220Z
65a0aade5fafc248c2156e95
https://cdn-avatars.huggingface.co/v1/production/uploads/65a0aade5fafc248c2156e95/S9YjJMTuKc-U1cFizqUMA.jpeg
DeyangKong
DeyangKong
user
DeyangKong
698bf2606052d3bed9630a5a
Xiangyu Xi
698bf2606052d3bed9630a5b
Jianfei Zhang
698bf2606052d3bed9630a5c
Yi Lu
698bf2606052d3bed9630a5d
Jingang Wang
698bf2606052d3bed9630a5e
Wei Wang
698bf2606052d3bed9630a5f
Shikun Zhang
698bf2606052d3bed9630a60
Wei Ye
OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration
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.
Reinforcement learning with verifiable rewards is used to enhance parallel thinking in large reasoning models through outline-guided path exploration that reduces information redundancy and improves solution discovery.
parallel thinking large reasoning models Reinforcement Learning RLVR mutual information bottleneck outline-guided path exploration reasoning outlines iterative RL strategy aggregation phase path exploration stage