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DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models

226 upvotes · 8 OCT 2026 · Zhenglin Wan, Jiayu Chen, Zhengfeiyang Zhang et al.

This paper introduces a new method for reinforcement learning with diffusion models, called DiffusionART, which combines the benefits of forward-process and reverse-process methods to improve efficiency and stability. Practitioners might care about this method because it can train diffusion models more effectively and efficiently than existing methods.

reinforcement learningdiffusion modelsstable diffusionscalable trainingtrust-region methods