@inproceedings{guo-etal-2026-ope,
title = "{OPE}: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration",
author = "Guo, Qi and
Wang, Jianing and
Kong, Deyang and
Xi, Xiangyu and
Zhang, Jianfei and
Lu, Yi and
Wang, Jingang and
Wang, Wei and
Zhang, Shikun and
Ye, Wei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1490/",
pages = "31708--31722",
ISBN = "979-8-89176-462-0",
abstract = "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. OPE are implemented 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."
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%0 Conference Proceedings
%T OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration
%A Guo, Qi
%A Wang, Jianing
%A Kong, Deyang
%A Xi, Xiangyu
%A Zhang, Jianfei
%A Lu, Yi
%A Wang, Jingang
%A Wang, Wei
%A Zhang, Shikun
%A Ye, Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F guo-etal-2026-ope
%X 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. OPE are implemented 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.
%U https://aclanthology.org/2026.findings-emnlp.1490/
%P 31708-31722
Markdown (Informal)
[OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration](https://aclanthology.org/2026.findings-emnlp.1490/) (Guo et al., Findings 2026)
ACL
- Qi Guo, Jianing Wang, Deyang Kong, Xiangyu Xi, Jianfei Zhang, Yi Lu, Jingang Wang, Wei Wang, Shikun Zhang, and Wei Ye. 2026. OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 31708–31722, Budapest, Hungary. Association for Computational Linguistics.