Any-OPD: Heterogeneous On-Policy Distillation for Flow-Matching Models via Representation-Space Bridging
On-policy distillation, in which a teacher corrects samples that the student itself generates, presupposes that the two models speak the same language: identical VAE latents, matching architectures, and a common timestep grid. We ask what happens when none of this holds, as when the strongest teacher available and the student one wishes to deploy come from different model families, and find that the standard recipes have no answer: teacher latents cannot serve as targets in a foreign coordinate
Record details
Published: 3 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Regulation · Children & education
Retrieved: 5 August 2026
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How to cite this record
ethics.ai (3 August 2026), “Any-OPD: Heterogeneous On-Policy Distillation for Flow-Matching Models via Representation-Space Bridging,” evidence record 16226, https://ethics.ai/record/16226 (originally published by HuggingFace Daily Papers).
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