SOURCE-LINKED INTELLIGENCE
Self-OPD: On-Policy Distillation for Flow Matching Models without Teacher
On-policy distillation (OPD), which leverages a pre-trained, specialized teacher model to provide dense supervisory signals, has achieved significant success in Large Language Models (LLMs) and has recently been adapted to flow matching models. However, this paradigm suffers from two major issues: First, training a separate, task-specific teacher for every new objective incurs high computational costs. Second, the discrepancy between teacher and student distributions often leads to compounding errors along the generation trajectory. In this paper, we introduce \textbf{Self-OPD}, a teacher-free
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T09:34:23.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.