Visual-Advantage On-Policy Distillation for Vision-Language Models
On-policy knowledge distillation has proven effective for language models, yet its application to vision-language models (VLMs) remains underexplored. We observe that standard on-policy distillation can improve a student's output quality while failing to strengthen its reliance on visual input: on vision-critical tokens, the student's predictions remain largely unchanged whether or not fine-grained visual detail is present, even though the teacher's predictions depend heavily on it.To make this difference observable, we introduce visual advantage (VA), the token-level log-probability difference when the teacher scores a student-generated rollout with versus without access to fine-grained ...
| Authors | Ruiqi Liu, Xiaolei Lv, Gengsheng Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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