Improved Distributional Diffusion Models with Late Particle Expansion

New Distributional Diffusion Models (DDMs) reduce scaling overhead by deferring multi-particle expansion to late transformer layers. By introducing time-dependent scoring rule schedules, the models resolve the trade-off between sampling budget and stochastic approximation accuracy.