Diffusion models are typically easier to control than autoregressive models because they can be evaluated and steered progressively during generation, while autoregressive outputs generally must be completed before they can be scored against constraints or objectives.

“it could be that that I think that's actually a very interesting point is that we know that diffusion models are typically easier to control compared to autoregressive models and the reason is that if you think about an autoregressive model you kind of like have to wait until you've generated the full object to know whether or not it satisfies let's say a constraint or whether or not”

“whether it's aligned or whether or not whatever you know it's on brand whatever it is that the objective function that you care about maybe you're generating a molecule and you care about solubility and then and then you kind of like have to wait until you have the full object to be able to score it with some reward function but a diffusion model it's more course to find generation so”

“from the very beginning you know kind of like is this object the kind of thing I want or not and you can steer this”