title: Reverse Thinking Makes LLMs Stronger Reasoners
summary: Reverse thinking plays a crucial role in human reasoning. Humans can reason
  not only from a problem to a solution but also in reverse, i.e., start from the
  solution and reason towards the problem. This often enhances overall reasoning
  performance as it enables consistency checks between their forward and backward
  thinking. To enable Large Language Models (LLMs) to perform reverse thinking,
  we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data
  augmentation and learning objectives. In RevThink, we augment the dataset by
  collecting structured forward-backward reasoning from a teacher model,
  consisting of: (1) the original question, (2) forward reasoning, (3) backward
  question, and (4) backward reasoning. We then employ three objectives to train
  a smaller student model in a multi-task learning fashion: (a) generate forward
  reasoning from a question, (b) generate a backward question from a question,
  and (c) generate backward reasoning from the backward question. Experiments
  across 12 datasets covering commonsense, math, and logical reasoning show an
  average 13.53% improvement over the student model's zero-shot performance and a
  6.84% improvement over the strongest knowledge distillation baselines.
  Moreover, our method demonstrates sample efficiency -- using only 10% of the
  correct forward reasoning from the training data, it outperforms a standard
  fine-tuning method trained on 10x more forward reasoning. RevThink also
  exhibits strong generalization to out-of-distribution held-out datasets.
paper_authors._id: 674d1b7c6a8ca5d12c06f3ae
paper_authors.name: Justin Chih-Yao Chen
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paper_authors.fullname: Justin Chen
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paper_authors.name: Zifeng Wang
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paper_authors.fullname: Zifeng Wang
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paper_authors.name: Hamid Palangi
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paper_authors.name: Rujun Han
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paper_authors.name: Sayna Ebrahimi
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paper_authors.name: Long Le
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paper_authors.name: Vincent Perot
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paper_authors.name: Swaroop Mishra
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paper_authors.fullname: Swaroop Mishra
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paper_authors.name: Mohit Bansal
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paper_authors.fullname: Mohit Bansal
paper_authors.type: user
paper_authors.user: mohitbansal
paper_authors._id: 674d1b7c6a8ca5d12c06f3b7
paper_authors.name: Chen-Yu Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T16:10:57.700Z
paper_authors._id: 64d1fe784dfd5df7076bfd1a
paper_authors.avatarUrl: /avatars/e2bb108d1f6b1383f0e3f263c4747b3d.svg
paper_authors.fullname: Chen-Yu Lee
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paper_authors._id: 674d1b7c6a8ca5d12c06f3b8
paper_authors.name: Tomas Pfister
paper_title: Reverse Thinking Makes LLMs Stronger Reasoners
paper_summary: Reverse thinking plays a crucial role in human reasoning. Humans can reason
  not only from a problem to a solution but also in reverse, i.e., start from the
  solution and reason towards the problem. This often enhances overall reasoning
  performance as it enables consistency checks between their forward and backward
  thinking. To enable Large Language Models (LLMs) to perform reverse thinking,
  we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data
  augmentation and learning objectives. In RevThink, we augment the dataset by
  collecting structured forward-backward reasoning from a teacher model,
  consisting of: (1) the original question, (2) forward reasoning, (3) backward
  question, and (4) backward reasoning. We then employ three objectives to train
  a smaller student model in a multi-task learning fashion: (a) generate forward
  reasoning from a question, (b) generate a backward question from a question,
  and (c) generate backward reasoning from the backward question. Experiments
  across 12 datasets covering commonsense, math, and logical reasoning show an
  average 13.53% improvement over the student model's zero-shot performance and a
  6.84% improvement over the strongest knowledge distillation baselines.
  Moreover, our method demonstrates sample efficiency -- using only 10% of the
  correct forward reasoning from the training data, it outperforms a standard
  fine-tuning method trained on 10x more forward reasoning. RevThink also
  exhibits strong generalization to out-of-distribution held-out datasets.
paper_ai_summary: RevThink, a framework combining data augmentation and multi-task learning, enhances reverse thinking in Large Language Models, leading to improved zero-shot performance and generalization.
paper_ai_keywords: Reverse-Enhanced Thinking RevThink data augmentation forward reasoning backward reasoning multi-task learning zero-shot performance knowledge distillation sample efficiency generalization