VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2607.28590 (cs)
[Submitted on 30 Jul 2026]
Title:VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation
Authors:Kangning Zhang, Yixing Li, Shuai Shao, Qingyao Li, Zhengxi Lu, Zhiyuan Yao, Jianghao Lin, Wenxiang Jiao, Yuan Lu, Weiwen Liu, Weinan Zhang, Yong Yu
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Abstract:Multimodal on-policy distillation (OPD) transfers fine-grained visual knowledge by supervising student-generated trajectories with a privileged-view teacher. Yet its next-token corrections are source-mixed, combining visual signals with linguistic priors and teacher-specific effects. The key challenge is to estimate which corrections are supported by visual evidence, not merely where or how strongly to distill. We introduce Visual Attribution Distillation (VAD), a counterfactual target-reconstruction algorithm that estimates the visually attributable part of a teacher correction. At each student-generated prefix, VAD evaluates the same fixed teacher with the relevant evidence present and removed. The corresponding change in centered log-probabilities defines ut, a signed proxy for the visual evidence direction that estimates how revealing the evidence supports or refutes candidate tokens. VAD projects the original correction onto this proxy to obtain an intervention-aligned component and a proxy-unexplained residual, then reconstructs a student-anchored target from the former. During training, this reconstructed target supplies the primary supervision signal, while the privileged teacher contributes a weak regularizer. Across six fine-grained visual benchmarks at 4B and 9B scales, VAD outperforms direct privileged-view distillation and visual-advantage weighting. Token- level and controlled-target analyses show that the proxy-aligned component is enriched in task-relevant visual corrections and yields stronger target shifts, especially when evidence refutes a mistaken answer. These results support counterfactual target reconstruction as an effective alternative to source-mixed supervision.
| Comments: | The project is accessible at this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.28590 [cs.CV] |
| (or arXiv:2607.28590v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28590
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation, by Kangning Zhang and 11 other authors
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