Morteza Mardani

We present an algorithm for test-time scaling of SDE-based diffusion models by searching for noise trajectories which optimize arbitrary rewards, empirically matching/exceeding MCTS performance.

A sequence-level EDM-derived diffusion model using progressively increasing noise levels is designed for probabilistic forecasting of complex dynamics like weather.