DanceOPD: On-Policy Generative Field Distillation
- Published
- Source
- arXiv
- Paper number
- 500
- Field
- Computer Vision
- arXiv ID
- 2606.27377
Key points
- The method formalizes the conflict between text-to-image, local editing, and global editing from a velocity-field perspective and resolves it with on-policy distillation.
- It removes target-field ambiguity with hard routing that assigns exactly one capability field to each sample.
- On GEditBench, it improves by 8.1 percent over the existing OPD baseline while preserving GenEval performance comparable to the T2I source.
- For local-plus-global editing combinations, it improves by 16.1 percent over the best competing baseline and by 7.9 percent over the local-edit source.
- When absorbing the realism field, it improves by 9.9 percent over off-policy distillation and reaches 85.3 percent of the student-teacher reward gap.
- It also shows that classifier-free guidance can be absorbed under the same velocity-MSE objective.
Paper links
External research summaries. These are not HDATF publications or measured product results.