ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning

Understand

We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid.

• Uniform and hand-crafted schedules are standard choices, but they rely on fixed prescriptions and can therefore be suboptimal.
• To address this limitation, we propose Adaptive Reparameterized Time (ART), a continuous-time control formulation that learns a time change by treating the speed of the sampling clock as the control, so that a uniform grid on the learned clock induces adaptive timesteps in the original diffusion time.
• Based on a leading-order Euler error surrogate, ART provides a principled objective for allocating timesteps along the sampling trajectory.