Reverse Thinking Makes LLMs Stronger Reasoners (2411.19865v2)

Abstract: 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 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.

• The paper introduces the RevThink framework that integrates reverse reasoning with forward problem-solving to improve LLM consistency and accuracy.
• The methodology employs a teacher-student multi-task learning approach, achieving an average improvement of 13.53% over baseline models.
• Experiments on 12 diverse datasets demonstrate that reverse reasoning enhances LLM robustness, offering both practical and theoretical advancements.

Reverse Thinking Makes LLMs Stronger Reasoners: An In-depth Analysis

The paper "Reverse Thinking Makes LLMs Stronger Reasoners" proposes an innovative framework, Reverse-Enhanced Thinking (RevThink), aimed at enhancing the reasoning performance of LLMs by incorporating reverse reasoning capabilities. This methodology is an advancement in training approaches for LLMs, introducing a bidirectional reasoning process that combines forward and backward problem-solving strategies.

Core Contributions

The primary contribution of this paper is the introduction of the RevThink framework, which leverages reverse reasoning—beginning from the solution and reasoning backwards to the problem—complementing traditional forward reasoning. This method enables more robust consistency checks and error mitigation in problem-solving tasks, an approach inspired by human reasoning processes.

RevThink involves a multi-task learning framework where a smaller student model is trained using a dataset augmented by a more capable teacher model. The augmented data includes structured reasoning chains that comprise both forward and backward solving modalities. The tasks imposed on the student model are:

• Forward reasoning generation from a question.
• Backward question generation from a question.
• Backward reasoning from a backward question.

Quantitative Improvements

The results from experiments conducted across 12 datasets in domains such as commonsense, mathematics, and logical reasoning are noteworthy. RevThink achieved an average improvement of 13.53% over the baseline zero-shot performance of the student models, and a 6.84% increase over leading symbolic knowledge distillation baselines. These results highlight RevThink's efficacy in not only enhancing accuracy but also its sample efficiency. The method surpasses traditional fine-tuning techniques, requiring only a fraction of the data to achieve superior performance.

Practical and Theoretical Implications

RevThink has significant implications for both practical applications and theoretical advancements in AI:

• Practical Implications: The framework's ability to generalize well to out-of-distribution datasets suggests potential applicability in real-world scenarios where input structures might not perfectly match the training data.
• Theoretical Implications: RevThink challenges the existing paradigms of LLM training by emphasizing the integration of bidirectional reasoning. The structured manner in which backward questions and reasoning are generated and validated introduces a novel approach to improving model robustness and understanding.

Future Directions

The prospect of incorporating reverse reasoning could lead to groundbreaking improvements in the capabilities of LLMs. Future research may explore:

• Extending RevThink to different types of models and more complex domains to evaluate scalability and adaptability.
• Enhancing the quality of backward reasoning generation to ensure high fidelity in reasoning consistency, potentially through better teacher model architectures or more sophisticated generation techniques.
• Investigating more fine-grained control over backward reasoning to understand its full potential in augmenting LLM reasoning abilities.

In conclusion, the RevThink framework represents a compelling development in LLM reasoning improvement, demonstrating significant performance gains. Its innovative use of reverse thinking offers a promising pathway for creating more reliable and generalizable AI systems. As research continues, it will be intriguing to observe how these methods are refined and applied in practice, potentially reshaping the landscape of AI reasoning methodologies.