AI 自己教自己，参考答案没那么重要
让 AI 自己教自己，很多人以为给老师一份标准答案，学生能学得更好。这篇论文用 5,319 道数学题做了严格对照，结果出乎意料：去掉参考答案、只靠老师直接给答案的推理过程，学生模型的提升几乎一样大。参考答案只在少数情况下有点用，而且换个模型就失效。更反直觉的是，让老师用更长的思考过程来教，反而让学生的成绩变差。这说明 AI 自我提升靠的不是答案本身，而是推理方式在不同模式间的迁移。它不是你明天能用上的东西，但它提醒我们：AI 的进步可能比我们以为的更依赖内在机制，而不是外部信息。
📄 原文摘要(英文)
On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution. Giving the teacher this extra information seems to offer the student more to learn, but how much does it add beyond distillation itself? To isolate that contribution, we construct AMPLE-Math, a reusable suite of 5,319 mathematical problems with six reasoning views that share the same answer, and compare each view with matched reference-free distillation. With a thinking-enabled teacher supervising direct-response rollouts, reference-free distillation accounts for much of Qwen3-1.7B's improvement under thinking-enabled evaluation, both in domain and on external benchmarks. Evidence for an additional reference benefit is modest in Qwen, strongest for a polished solution, whereas complete traces add two percentage points in SmolLM3-3B at step 50. These benefits depend on the student being trained. At the same checkpoint, replacing short direct-response rollouts with long thinking-enabled rollouts turns gains into losses in both families while the problems, references, and evaluation stay fixed. Teacher profiles and matched loss interventions in Qwen further show that changing token-level supervision can leave student behavior largely unchanged. Together, these findings suggest that OPSD can improve access to existing reasoning capabilities through parameters shared by direct-response and thinking-enabled inference. The value of a privileged reference is what it adds to this cross-mode transfer, not how much of the solution it reveals.