Optimal Self-Distillation for Rectified Flow via Linear Probing
Modern generative models are increasingly trained using model-generated signals, creating both opportunities for self-improvement and risks of collapse. We study optimal self-distillation (SD) for rectified flow (RF): given a suboptimal teacher velocity field, can a student trained on a mixture of true RF velocities and teacher velocities provably improve the teacher? For linear RF with ridge regularization on fixed interpolation pairs, we prove an exact affine path identity, derive the optimal mixing coefficient in closed form, and show strict improvement in integrated velocity risk whenever the teacher risk is nonstationary along the regularization path. The optimal coefficient obeys a ...
| Authors | Saptarshi Roy, Debepsita Mukherjee, Pratik Patil |
|---|---|
| Primary category | stat.ML |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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