Dwarkesh Patel

It's not a goal about the external world.

I guess maybe the bigger question I want to understand is why you don't think doing RL on top of LLMs is a productive direction.

Because we seem to be able to give these models the goal of solving difficult math problems.

And they're in many ways at the very peaks of human level in the capacity to solve Math Olympia-type problems, right?

They got gold at IMO.

So it seems like the model which got gold at the International Math Olympia does have the goal of getting math problems, right?

So why can't we extend this to different domains?

Right.

So, I mean, it's interesting because you wrote this essay in 2019 titled The Bitter Lesson, and this is the most influential essay perhaps in the history of AI, but people have used that as a justification for,

for scaling up LLMs, because in their view, this is the one scalable way we have found to pour ungodly amounts of compute into learning about the world.

And so it's interesting that your perspective is that the LLMs are actually not bitter lesson told.

I guess that doesn't seem like the crux to me because I think those people would also agree that the overwhelming amount of compute in the future will come from learning from experience.

They just think that the scaffold or the basis of that

The thing you'll start with in order to pour in the compute to do this future experiential learning or on the job learning will be LLMs.

And so I guess I still don't understand why this is the wrong starting point altogether.

Why we need a whole new architecture to begin doing experiential continual learning and why we can't start with LLMs to do that.

Maybe it's interesting to compare this to humans.

So in both the case of learning from imitation versus experience and on the question of goals, I think there's some interesting analogies.

So, you know, kids will initially learn from imitation.

You don't think so?