大模型自己出题自己练，不需要人类答案了
大模型一直有个尴尬：想变强，得靠人类或更强的模型喂题喂答案。这篇把这条路堵死了——只给它几个粗方向(比如「物理」「逻辑」)，它自己编问题、自己写推理过程、自己批改，然后拿这套完全自产的数据再练一轮。结果在语言和视觉问答上都有提升，而且练了A领域不会忘掉B领域。它证明模型内部藏着大量没被用出来的能力，不需要外部老师也能挖出来。这不是你明天能用的功能，但它指向一个更省事的训练方向：以后调模型，可能连标注数据都不用攒了。
📄 原文摘要(英文)
On-policy self-distillation enables post-training foundation models without relying on stronger external teachers. However, existing approaches rely on ground-truth question-answer pairs, human-curated problem sets, or other supervised signals. In this work, we reformulate on-policy self-distillation as a self-training framework, driven by self-generated question-answer pairs and privileged reasoning demonstrations. We push supervision to the limit: guided solely by coarse, high-level topic descriptions, the model synthesizes its own curriculum of queries and privileged reasoning traces without access to any human-curated prompts or answers. We find that self-distillation on fully synthetic privileged information enables a model to improve across multiple domains while retaining its capabilities on domains it is not self-trained on. We demonstrate the generality of this framework across text and multimodal domains, achieving consistent self-improvement on standard language benchmarks and Visual Question Answering. Our findings demonstrate that foundation models possess rich latent capabilities that can be amplified without external task supervision or curated problem distributions.