AI自己教自己：不看答案，只看图片差异
训练视觉AI通常需要老师模型或额外标注，但这篇论文让AI自己当自己的老师，而且不需要任何外部信息。方法很简单：给模型看原图和一张被抹掉内容的图，对比两者在生成回答时的概率差异——那些因为看到原图才更可能被选中的词，就是模型真正依赖的视觉线索。然后让模型强化这些线索，忽略噪声。在多个模型上，这个技巧让综合性能提升了2到5个百分点，且推理时不增加任何计算成本。它不是你明天能用上的，但指向一个趋势：AI训练正从“喂数据”转向“设计对比信号”，让模型自己学会什么值得看。
📄 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.