Vision-Language Models Can Self-Improve Reasoning via Reflection

• URL: http://arxiv.org/abs/2411.00855v1
• Date: Wed, 30 Oct 2024 14:45:00 GMT
• Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
• Authors: Kanzhi Cheng, Yantao Li, Fangzhi Xu, Jianbing Zhang, Hao Zhou, Yang Liu,
• Abstract summary: Chain-of-thought (CoT) has proven to improve the reasoning capability of large language models (LLMs) We propose a self-training framework, R3V, which iteratively enhances the model's Vision-language Reasoning by Reflecting on CoT Rationales. Our approach supports self-reflection on generated solutions, further boosting performance through test-time computation.
• Score: 20.196406628954303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-thought (CoT) has proven to improve the reasoning capability of large language models (LLMs). However, due to the complexity of multimodal scenarios and the difficulty in collecting high-quality CoT data, CoT reasoning in multimodal LLMs has been largely overlooked. To this end, we propose a simple yet effective self-training framework, R3V, which iteratively enhances the model's Vision-language Reasoning by Reflecting on CoT Rationales. Our framework consists of two interleaved parts: (1) iteratively bootstrapping positive and negative solutions for reasoning datasets, and (2) reflection on rationale for learning from mistakes. Specifically, we introduce the self-refine and self-select losses, enabling the model to refine flawed rationale and derive the correct answer by comparing rationale candidates. Experiments on a wide range of vision-language tasks show that R3V consistently improves multimodal LLM reasoning, achieving a relative improvement of 23 to 60 percent over GPT-distilled baselines. Additionally, our approach supports self-reflection on generated solutions, further boosting performance through test-time computation.

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