Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
Existing reinforcement learning methods for diffusion models are fragmented because they use different loss functions derived from different principles. This work unifies them by showing that they stem from a single path-space principle. By using importance sampling between SDEs, the authors derive a variance-reduced objective and propose a unified design space covering value estimation, weight functions, and sampling choices.