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

Sergey Levine discusses how diffusion and flow models have transformed continuous-action policies in reinforcement learning and robotic control.

Sergey Levine (UC Berkeley) simons.berkeley.edu/talks/sergey-levine-uc-berkeley-2026-08-… Diffusion Generative Modeling: Progress and Next Steps In this talk, I'll discuss how diffusion and flow models transformed continuous-action policies in RL and robotic control. While in principle learning-based control methods (imitation learning and RL) are largely agnostic to the particular representation used for the action distribution, in practice diffusion and flow models have significantly improved the performance of learned policies across various learning-based control domains. By modeling complex distributions over high-dimensional spaces, these models made it possible to represent large "action chunks" (sequences of actions), which greatly improved imitation learning performance and, most recently, the performance of offline RL and offline-to-online RL methods. In this talk, I'll discuss algorithms that use diffusion and flow for RL and IL, as well as some of the large-scale models built on these principles.