One appeal of it is for RL. If it ends up being a lot faster for generation, you'll be able to do a lot more RL.

If people can make RL scalable-- make it so that RL isn't just a final phase, but something which is as big as the supervised stuff, then diffusion models are going to have an advantage.

If not, I think autoregressive models will still be preferred. Diffusion models become fixed very fast, they can't actually refine their outputs, so we're not talking about some kind of refinement along the lines of: initial idea -> better idea -> something actually sound.