but diffusion isn’t the strongest. diffusion planning is still complete trash compared to mcts at finding very narrow wedges of possibility space that have high consequential value. it’s not even vaguely the same ballpark. you can’t guide diffusion planning like you can guide mcts unless you turn diffusion into mcts. diffusion is good for a generative model, but its compute depth is fundamentally wrong. if you want to optimize something REALLY REALLY INHUMANLY HARD, mcts style techniques are how you do it right now; but of course they fall into adversarial examples of most world models you feed them, because they optimize too hard for the world model to retain validity. and isn’t that the very thing we’re worried about from safety—a very strong planner that hasn’t learned to keep itself moving back towards the actually-truly-prosocial-for-real part of the natural abstraction for plan space?
I’m not sure which diffusion planning you are talking about—some current version? Future versions? My version?
MCTS doesn’t scale in the way that full neural planning can scale. So MCTS is mostly irrelevant for AGI, as the latter requires highly scalable approximate planning. So to me the question is simply what is the highly scalable approximate neural planner look like? And my best answer for that of course is a secret, and my best public answer is something like diffusion planning.
but diffusion isn’t the strongest. diffusion planning is still complete trash compared to mcts at finding very narrow wedges of possibility space that have high consequential value. it’s not even vaguely the same ballpark. you can’t guide diffusion planning like you can guide mcts unless you turn diffusion into mcts. diffusion is good for a generative model, but its compute depth is fundamentally wrong. if you want to optimize something REALLY REALLY INHUMANLY HARD, mcts style techniques are how you do it right now; but of course they fall into adversarial examples of most world models you feed them, because they optimize too hard for the world model to retain validity. and isn’t that the very thing we’re worried about from safety—a very strong planner that hasn’t learned to keep itself moving back towards the actually-truly-prosocial-for-real part of the natural abstraction for plan space?
I’m not sure which diffusion planning you are talking about—some current version? Future versions? My version?
MCTS doesn’t scale in the way that full neural planning can scale. So MCTS is mostly irrelevant for AGI, as the latter requires highly scalable approximate planning. So to me the question is simply what is the highly scalable approximate neural planner look like? And my best answer for that of course is a secret, and my best public answer is something like diffusion planning.