Grant Harvey

Yeah, yeah.

That makes sense.

So which one would be more expensive, I guess, with like a smaller model thinking for longer or bigger model thinking for shorter?

Right.

That's tricky.

Right.

I guess it makes me wonder, like if you were to like, let's say like the next scaling paradigm, whatever, whatever the next step up from here is, um, would that model be incredibly hard to, to read its traces?

Well, I guess I should clarify.

Let's say if we go off of the previous scaling paradigm, which is just size and data, right?

Compute and data.

If you were to scale up to the next size up of compute and data, if that were even possible...

Would that model not think for very long?

And then would that then make it, it makes me think that that model's thinking traces would be even harder to interpret because it's, you know, following the logic of what you've proven so far.

As it scales.

Yeah, it's a bigger model and it's thinking for less time to get to the right answer.

So speaking of open windows into the mind, you know, we're looking at the thoughts here.

You also published a paper recently on weight sparse transformers where you prune the model to make its internal circuits easier to see.

How does like, I guess, mechanistic interpretability and chain of thought monitor ability work?

Right.

You at least contributed to the system, the model card for it, right?