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arXiv Computer Vision By Wen Luo, Xiaohan Yi, Xiaotao Huang, Liqun Huang

VIG: Visual Information Gain as a Reward Signal for Multimodal Chain-of-Thought Compression

Read the original on arXiv Computer Vision →

The paper introduces VIG (Visual Information Gain), an information‑theoretic reward that evaluates each token in a multimodal chain‑of‑thought by measuring how much the image reduces its predictive uncertainty. VIG is computed online using two forward passes—one with and one without the image—eliminating the need for reference chains or external annotations. Experiments on six multimodal reasoning benchmarks and multiple Qwen3‑VL‑Thinking model sizes show that VIG consistently improves the accuracy–efficiency trade‑off, demonstrating that efficient multimodal reasoning arises from increasing visual information density rather than merely limiting chain length.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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