在線蒸餾顯示小樣本也能傳遞推理方式

Computer Science > Machine Learning arXiv:2609.

14193 (cs) [Submitted on 12 Sep 2026 (v1), last revised 17 Sep 2026 (this version, v2)] Title:Data-free On-policy Distillation Authors:Gengsheng Li, Mao Zheng, Mingyang Song, Jie Sun, Zeyuan Liu, Ruiqi Liu, Qiyong Zhong, Haiyun Guo, Junfeng Fang, Jinqiao Wang View a PDF of the paper titled Data-free On-policy Distillation, by Gengsheng Li and 9 other authors View PDF HTML (experimental) Abstract:On-policy distillation (OPD) has become a standard component of frontier post-training pipelines, yet how much its training data actually contributes has gone largely unexamined.

On the two teacher--student pairings most common in practice, we find OPD almost indifferent to its data: eight prompts already match a 17k-problem dataset, and three independently built datasets whose difficulty and teacher--student KL differ several-fold produce nearly indistinguishable training curves.

Two causes account for this.First, the unit of data in OPD is the state a prompt leads to, not the prompt itself: a single prompt keeps exposing new teacher correction as sampling continues, while the marginal value of additional prompts collapses after eight.

Second, replacing mathematics with competitive programming still recovers over ninety percent of the in-domain gain, indicating that OPD transfers the teacher's mode of reasoning rather than knowledge related to the data.

We take this to its limit with \textbf{Data-free On-policy Distillation} (DF-OPD), in which the teacher writes its own training questions under a simple prompt---no external data, no quality filtering---leaving a system of just two policies.

DF-OPD matches and even surpasses real data, and the questions it produces track the teacher's own post-training data on three key diagnostics of training dynamics, which other real datasets do not.

Applied to multi-teacher distillation, where the (prompt, domain) pairs normally have to be derived from post-training data that is often out of reach, 1k self-generated questions close 98.5\% of the available headroom, even surpassing the 96.6\% reached with 7k real examples.

Together these results invite a reassessment of the role data plays in OPD.Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.14193 [cs.LG] (or arXiv:2609.14193v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.

14193 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Gengsheng Li [view email] [v1] Sat, 12 Sep 2026 23:51:35 UTC (320 KB) [v2] Thu, 17 Sep 2026 20:45:40 UTC (453 KB) Full-text links: Access Paper: View a PDF of the paper titled Data-free On-policy Distillation, by Gengsheng Li and 9 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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