The speaker describes diffusion models as non-parametric explanations in which models fit a density, and says his reinforcement-learning work can also be characterized as fitting a density.

“Also, stuff like diffusion like diffusion is also it's a non parametric uh exper uh explanation of what these models do. You just say, well, they fit some density. Reinforcement learning, what I do, it's also they fit some density, be happy. But uh the the actual loop should be to actually then take this and trying to make inference for the real world. Like if I if I prove something converges and I test it and it doesn't”