@inproceedings{xu-etal-2025-reason,
title = "Reason from Future: Reverse Thought Chain Enhances {LLM} Reasoning",
author = "Xu, Yinlong and
Zheng, Yanzhao and
Sun, Shuoshuo and
Huang, Shuaihan and
Dong, Baohua and
Hangcheng, Zhu and
Huang, Ruohui and
Yu, Gang and
Xu, Hongxia and
Wu, Jian",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1290/",
doi = "10.18653/v1/2025.findings-acl.1290",
pages = "25153--25166",
ISBN = "979-8-89176-256-5",
abstract = "It has been demonstrated that carefully designed reasoning paradigms, like Chain-of-Thought(CoT) and Tree-of-Thought(ToT), can enhance the reasoning capabilities of small language models by detailed thinking and extensive thought searching, unbounded branching factors in the searching space create prohibitive reasoning consumption. However these methods fell into the trap of local optimum reasoning, which means the model lacks a global perspective while solving problems. We propose a novel reasoning paradigm called Reason from Future(RFF), which generates reasoning paths by bidirectional reasoning that combines top-down planning with bottom-up reasoning accumulation. The essence of RFF lies in its reverse reasoning mechanism, which prioritizes core logical relationships and imposes goal-oriented constraints on intermediate steps, thereby reducing the searching space and mitigating error accumulation inherent in sequential forward reasoning. Empirical evaluations across diverse experiments demonstrate that RFF outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks."
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<abstract>It has been demonstrated that carefully designed reasoning paradigms, like Chain-of-Thought(CoT) and Tree-of-Thought(ToT), can enhance the reasoning capabilities of small language models by detailed thinking and extensive thought searching, unbounded branching factors in the searching space create prohibitive reasoning consumption. However these methods fell into the trap of local optimum reasoning, which means the model lacks a global perspective while solving problems. We propose a novel reasoning paradigm called Reason from Future(RFF), which generates reasoning paths by bidirectional reasoning that combines top-down planning with bottom-up reasoning accumulation. The essence of RFF lies in its reverse reasoning mechanism, which prioritizes core logical relationships and imposes goal-oriented constraints on intermediate steps, thereby reducing the searching space and mitigating error accumulation inherent in sequential forward reasoning. Empirical evaluations across diverse experiments demonstrate that RFF outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks.</abstract>
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%0 Conference Proceedings
%T Reason from Future: Reverse Thought Chain Enhances LLM Reasoning
%A Xu, Yinlong
%A Zheng, Yanzhao
%A Sun, Shuoshuo
%A Huang, Shuaihan
%A Dong, Baohua
%A Hangcheng, Zhu
%A Huang, Ruohui
%A Yu, Gang
%A Xu, Hongxia
%A Wu, Jian
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F xu-etal-2025-reason
%X It has been demonstrated that carefully designed reasoning paradigms, like Chain-of-Thought(CoT) and Tree-of-Thought(ToT), can enhance the reasoning capabilities of small language models by detailed thinking and extensive thought searching, unbounded branching factors in the searching space create prohibitive reasoning consumption. However these methods fell into the trap of local optimum reasoning, which means the model lacks a global perspective while solving problems. We propose a novel reasoning paradigm called Reason from Future(RFF), which generates reasoning paths by bidirectional reasoning that combines top-down planning with bottom-up reasoning accumulation. The essence of RFF lies in its reverse reasoning mechanism, which prioritizes core logical relationships and imposes goal-oriented constraints on intermediate steps, thereby reducing the searching space and mitigating error accumulation inherent in sequential forward reasoning. Empirical evaluations across diverse experiments demonstrate that RFF outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks.
%R 10.18653/v1/2025.findings-acl.1290
%U https://aclanthology.org/2025.findings-acl.1290/
%U https://doi.org/10.18653/v1/2025.findings-acl.1290
%P 25153-25166
Markdown (Informal)
[Reason from Future: Reverse Thought Chain Enhances LLM Reasoning](https://aclanthology.org/2025.findings-acl.1290/) (Xu et al., Findings 2025)
ACL
- Yinlong Xu, Yanzhao Zheng, Shuoshuo Sun, Shuaihan Huang, Baohua Dong, Zhu Hangcheng, Ruohui Huang, Gang Yu, Hongxia Xu, and Jian Wu. 2025. Reason from Future: Reverse Thought Chain Enhances LLM Reasoning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25153–25166, Vienna, Austria. Association for Computational Linguistics.