VIG: Visual Information Gain as a Reward Signal for Multimodal Chain-of-Thought Compression
Multimodal large reasoning models often rely on long Chain-of-Thought (CoT) traces in which a substantial fraction of tokens, such as repeated visual descriptions, self-reflection, and other visually-disengaged filler, inflate inference cost without contributing to the answer. Existing CoT compression methods optimize output length but never measure whether a reasoning token is actually grounded in the image. We propose \textbf{VIG} (Visual Information Gain), an information-theoretic GRPO reward that scores each reasoning token by how much the image reduces its predictive uncertainty. VIG is computed online from two forward passes of the same policy, one with and one without the image, so...
| Authors | Wen Luo, Xiaohan Yi, Xiaotao Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 August 2026 |
| Last updated at source | 27 August 2026 |
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