ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule

Understand

We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid.

• Uniform and hand-crafted grids can be suboptimal given a budget on the number of time steps.
• We introduce Adaptive Reparameterized Time (ART), which controls the clock speed of a reparameterized time variable to redistribute computation along the sampling trajectory while preserving the terminal time, with the objective of minimizing the aggregate Euler discretization error.
• We derive a randomized companion ART-RL that recasts ART as a continuous-time reinforcement learning problem with Gaussian policies, and prove a two-directional bridge between the two: the deterministic ART optimum lifts to an optimal Gaussian policy, and conversely any optimal Gaussian policy must recover the ART control through its mean.