AI自蒸馏新招：删掉图片内容，反而学得更好
训练视觉AI时，通常需要一个大老师模型来教小学生。这篇论文发现，让模型自己教自己反而更有效——方法很反直觉：把图片里的内容擦掉一部分，让模型对比“看到原图”和“看到擦除图”时的反应差异，差异大的地方就是模型真正需要关注的信息。用这个信号来强化学习，不需要外部老师、不需要额外推理成本，在多个模型上准确率提升2-5个百分点。它不是你明天能用上的，但展示了“自监督”的一种新思路：不是给模型更多信息，而是刻意制造信息差，让模型自己学会抓住重点。
📄 原文摘要(英文)
On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still needs asymmetric information between teacher and student to ensure that the self-teacher provides a stronger learning signal than the student. Existing methods create this asymmetry either through privileged answers or visual evidence. We ask whether both can be removed, yielding a simpler form of OPSD driven purely by input conditioning. For this purpose, we propose Visual Contrastive Self-Distillation, namely VCSD, which converts image-content removal into an on-policy self-distillation signal. At each student-generated response prefix, the EMA teacher produces two next-token distributions under the same prompt and prefix -- one conditioned on the original image and the other on a content-erased control. Their token-wise log-probability difference highlights candidates whose likelihood is specifically increased by the instance-level visual content. We use this contrast to sharpen the teacher's original-image distribution within its plausible support, and distill the resulting full-distribution target into the student. Using ViRL39K dataset, VCSD consistently outperforms matched OPSD across Qwen3-VL and Qwen3.5 models. For example, on Qwen3-VL, it improves the seven-benchmark aggregate from 62.27% rightarrow 67.04% at 2B, 71.30% rightarrow 73.16% at 4B, and 72.51% rightarrow 76.26% at 8B. Furthermore, VCSD requires no external teacher, privileged answers, visual evidence signals, reasoning traces, or additional inference-time cost.