Self-Supervised Visual On-Policy Distillation
Summary
The paper introduces S^2VOPD, a self-supervised method that improves vision-language models by distilling from original images into strongly augmented student views, achieving performance surpassing GPT-5.4 on benchmarks.
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Paper page - Self-Supervised Visual On-Policy Distillation
Source: https://huggingface.co/papers/2608.14144
Abstract
Self-supervised visual on-policy distillation improves small vision-language models by distilling from original images into strongly augmented student views without privileged annotations or larger teachers.
Visual on-policy distillationrelies heavily on an informativeteacher-student asymmetry, through either a larger, stronger teacher orprivileged supervision, such as reference answers or ground-truth regions of interest. This raises a fundamental question: where can informative asymmetry come from when nothing privileged is available? We answer this by inverting where the asymmetry comes from. Rather than adding privileged information to the teacher, we subtract information from the student. This asymmetry creates the same effective learning signal for free as a teacher with access to information unavailable to the student, without ground-truth annotations, rewards, or a separate stronger teacher model. Building on this principle, we introduceSelf-Supervised Visual On-Policy Distillation(S^2VOPD), a simple yet effective method that constructs on-policy learning signals fromasymmetric augmented views. S^2VOPD distills the teacher’s distribution conditioned on the original image on-policy into the student distribution conditioned on a strongly augmented view of the same image. We systematically explore a broad design space of visual augmentations and uncover that (1) asymmetry matters: all four augmentation families improve performance, while symmetricself-distillationdegrades it; (2) strength matters: performance peaks at a moderate strength; and (3) the gap must remain task-consistent: augmentations that completely remove the question-relevant evidence can induce large but uninformative discrepancies. Across sixfine-grained perceptionbenchmarks, S^2VOPD improves Qwen3.5-4B from 70.7% to 77.4%, above all open-source models compared, up to Qwen3-VL at 235B, and surpasses GPT-5.4. While holding training data the same, it recovers 96% of the improvement achieved by methods with privileged information. Website is at https://williamium3000.github.io/s2vopd
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