Dwarkesh

I'm just saying it's not 100% obvious.

But what is that?

How do you think about emotions?

What is the ML analogy for emotions?

It might be worth defining for the audience what a value function is if you want to do that.

this was in the DeepSeq R1 paper, is that the space of trajectories is so wide that maybe it's hard to learn a mapping from an intermediate trajectory and value.

And also given that, you know, in coding, for example, you will have the wrong idea, then you'll go back, then you'll change something.

That's the thing I was actually planning on asking you.

There's something really interesting about emotions of the value function, which is that...

It's impressive that they have this much utility while still being rather simple to understand.

So I have two responses.

Yeah.

People have been talking about scaling data, scaling parameter, scaling compute.

Is there a more general way to think about scaling?

What are the other scaling axes?

That's a very interesting way to put it.

But let me ask you the question you just posed then.

What are we scaling and what would it mean to have a recipe?

Because I guess I'm not aware of a very clean relationship that almost looks like a law of physics, which existed in pre-training.

There was a power law between data or computer parameters and loss.