Diffusion in RL and robotics: how expressive policies changed how we use continuous actions

Diffusion and flow models provide expressive distributions over continuous actions in robotic control. This talk explains how modelling extended action sequences improved imitation learning and subsequently offline and offline-to-online reinforcement learning. It reviews algorithms built around these representations and large-scale control models that use them, connecting policy expressiveness with practical learning performance.