title: Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
    Language Models
summary: The integration of slow-thinking mechanisms into large language models (LLMs)
  offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified
  by systems like OpenAI's o1. However, several significant challenges remain,
  including inefficient overthinking and an overreliance on auxiliary reward
  models. We point out that these limitations stem from LLMs' inability to
  internalize the search process, a key component of effective reasoning. A
  critical step toward addressing this issue is enabling LLMs to autonomously
  determine when and where to backtrack, a fundamental operation in traditional
  search algorithms. To this end, we propose a self-backtracking mechanism that
  equips LLMs with the ability to backtrack during both training and inference.
  This mechanism not only enhances reasoning ability but also efficiency by
  transforming slow-thinking processes into fast-thinking through
  self-improvement. Empirical evaluations demonstrate that our proposal
  significantly enhances the reasoning capabilities of LLMs, achieving a
  performance gain of over 40 percent compared to the optimal-path supervised
  fine-tuning method. We believe this study introduces a novel and promising
  pathway for developing more advanced and robust Reasoners.
paper_authors._id: 67a97bc5500b3bcf5babc5e8
paper_authors.name: Xiao-Wen Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-10T09:49:57.842Z
paper_authors._id: 64bb3d1eb1a618880956da76
paper_authors.avatarUrl: /avatars/ec393b5eee8a3ccec61107b4aa63c4d9.svg
paper_authors.fullname: Xiao-Wen Yang
paper_authors.type: user
paper_authors.user: yangxw
paper_authors._id: 67a97bc5500b3bcf5babc5e9
paper_authors.name: Xuan-Yi Zhu
paper_authors._id: 67a97bc5500b3bcf5babc5ea
paper_authors.name: Wen-Da Wei
paper_authors._id: 67a97bc5500b3bcf5babc5eb
paper_authors.name: Ding-Chu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-10T16:34:44.713Z
paper_authors._id: 65542c8c9bd4907a067050b2
paper_authors.avatarUrl: /avatars/031a2fdcc7d73da4f88fbcfca6ad3920.svg
paper_authors.fullname: Zhang Dingchu
paper_authors.type: user
paper_authors.user: zhangdingchu
paper_authors._id: 67a97bc5500b3bcf5babc5ec
paper_authors.name: Jie-Jing Shao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-10T16:25:55.507Z
paper_authors._id: 640731714dc5f2846c945251
paper_authors.avatarUrl: /avatars/a15695d306f05dd10a7b7f636af6a4f5.svg
paper_authors.fullname: Jie-Jing Shao
paper_authors.type: user
paper_authors.user: shjj
paper_authors._id: 67a97bc5500b3bcf5babc5ed
paper_authors.name: Zhi Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-12T09:17:00.318Z
paper_authors._id: 64675fd0b990713c50317559
paper_authors.avatarUrl: /avatars/931ad545e6b889b5fa02a96411bcb2f3.svg
paper_authors.fullname: Zhi Zhou
paper_authors.type: user
paper_authors.user: WNJXYK
paper_authors._id: 67a97bc5500b3bcf5babc5ee
paper_authors.name: Lan-Zhe Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-10T16:25:47.379Z
paper_authors._id: 63fc116b1b4b1bd4e707d198
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63fc116b1b4b1bd4e707d198/kM1pL6_FUVwM2PXpNV160.jpeg
paper_authors.fullname: Lan-Zhe Guo
paper_authors.type: user
paper_authors.user: Guolz
paper_authors._id: 67a97bc5500b3bcf5babc5ef
paper_authors.name: Yu-Feng Li
paper_title: Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
    Language Models
paper_summary: The integration of slow-thinking mechanisms into large language models (LLMs)
  offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified
  by systems like OpenAI's o1. However, several significant challenges remain,
  including inefficient overthinking and an overreliance on auxiliary reward
  models. We point out that these limitations stem from LLMs' inability to
  internalize the search process, a key component of effective reasoning. A
  critical step toward addressing this issue is enabling LLMs to autonomously
  determine when and where to backtrack, a fundamental operation in traditional
  search algorithms. To this end, we propose a self-backtracking mechanism that
  equips LLMs with the ability to backtrack during both training and inference.
  This mechanism not only enhances reasoning ability but also efficiency by
  transforming slow-thinking processes into fast-thinking through
  self-improvement. Empirical evaluations demonstrate that our proposal
  significantly enhances the reasoning capabilities of LLMs, achieving a
  performance gain of over 40 percent compared to the optimal-path supervised
  fine-tuning method. We believe this study introduces a novel and promising
  pathway for developing more advanced and robust Reasoners.
paper_ai_keywords: self-backtracking mechanism backtracking search algorithm autonomous determination reasoning ability efficient reasoning performance gain optimal-path supervised fine-tuning method reasoning capabilities