Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
  Language Models
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.
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Xiao-Wen Yang
claimed_verified
2025-02-10T09:49:57.842Z
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Xiao-Wen Yang
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Xuan-Yi Zhu
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Wen-Da Wei
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Ding-Chu Zhang
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2025-02-10T16:34:44.713Z
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Zhang Dingchu
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Jie-Jing Shao
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2025-02-10T16:25:55.507Z
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Jie-Jing Shao
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Zhi Zhou
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2025-02-12T09:17:00.318Z
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Zhi Zhou
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Lan-Zhe Guo
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2025-02-10T16:25:47.379Z
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Lan-Zhe Guo
user
Guolz
67a97bc5500b3bcf5babc5ef
Yu-Feng Li
Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
  Language Models
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.
Introducing a self-backtracking mechanism to LLMs enhances reasoning efficiency and capability, outperforming supervised fine-tuning methods.
large language models (LLMs) Level 2 AGI Reasoners o1 overthinking auxiliary reward models search process self-backtracking mechanism training inference reasoning ability optimization performance gain optimal-path supervised fine-tuning method