Learning to Sample From Diffusion Models via Inverse Reinforcement Learning

Inverse reinforcement learning trains diffusion sampling schedules by matching target behavior via policy gradients, cutting ImageNet-64 tuning costs up to 9x versus grid search with 16% inference overhead.

Constant Bourdrez, Alexandre Verine, Olivier Cappé

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026