Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought

• URL: http://arxiv.org/abs/2507.07685v1
• Date: Thu, 10 Jul 2025 12:07:13 GMT
• Title: Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought
• Authors: Shin'ya Yamaguchi, Kosuke Nishida, Daiki Chijiwa,
• Abstract summary: Chain-of-thought (CoT) prompting has been adapted for large vision-language models (LLMs) to enhance multi-modal reasoning.<n>Existing LVLMs often ignore the contents of generated rationales in CoT reasoning.<n>We propose rationale-enhanced decoding (RED), a novel plug-and-play inference-time decoding strategy.
• Score: 11.538345159297839
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large vision-language models (LVLMs) have demonstrated remarkable capabilities by integrating pre-trained vision encoders with large language models (LLMs). Similar to single-modal LLMs, chain-of-thought (CoT) prompting has been adapted for LVLMs to enhance multi-modal reasoning by generating intermediate rationales based on visual and textual inputs. While CoT is assumed to improve grounding and accuracy in LVLMs, our experiments reveal a key challenge: existing LVLMs often ignore the contents of generated rationales in CoT reasoning. To address this, we re-formulate multi-modal CoT reasoning as a KL-constrained reward maximization focused on rationale-conditional log-likelihood. As the optimal solution, we propose rationale-enhanced decoding (RED), a novel plug-and-play inference-time decoding strategy. RED harmonizes visual and rationale information by multiplying distinct image-conditional and rationale-conditional next token distributions. Extensive experiments show that RED consistently and significantly improves reasoning over standard CoT and other decoding methods across multiple benchmarks and LVLMs. Our work offers a practical and effective approach to improve both the faithfulness and accuracy of CoT reasoning in LVLMs, paving the way for more reliable rationale-grounded multi-modal systems.

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