DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models

DiffusionART improves online reinforcement learning for diffusion models by aggregating prompt rewards into a shared direction for reverse-process states. This approach mitigates the instability of forward-process methods and addresses the poor credit assignment seen in reverse-process methods like Flow-GRPO by utilizing a hidden forward-reverse consistency force.