On-Policy Self-Distillation in Diffusion Models
Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction should change. We introduce DiffusionOPSD as an on-policy self-distillation framework that converts image-level reward guidance into explicit targets for clean-output predictions at sampled queries. At each outer iteration, a frozen behavior policy generates trajectories and supplies query states and anchors. Reward gradients construct bounded positive and negative targets around each anchor. The trainable policy fits these targets as detached supervision through finite fitting before an exponential moving avera...
| Authors | Wei Zhou, Xiongwei Zhu, Lingdong Kong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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