Robert Lange: prediction

Transcript context

…Yes. And it reminded me, I spoke to Lion about this. You've got this Sudoku bench. And a lot of folks watch cracking the cryptic YouTube channel. And that's exactly what they do. They invent new problems based on abstractions that capture the essence or aspects of the problem you're solving. And then they do something which is similar to Schinker Evolves. They do this kind of evolution where they take these different solutions and they kind of combine the best aspects of both of them. And they forge a divergent path to a new solution. Yeah. And that seems to be the essence of of what we need to do. Yeah. For sure. I I mean, is some work also by Jeff Cloon, Shengren Hu, and Son Lu on automatic automated capability discovery. So there, they look at language models that generate tasks. Right? But it's in a, let's say, unstructured way in the sense that it's not done in order to enable the solution to 1 target problem. Right? And I think sort of doing these connections is gonna be very fruitful down the line. Very cool. Now, other thing, we'll show the graph on the screen, the evolutionary graph. So for the circle back in problem. I was looking at that and first of all, it looked incredibly parsimonious, which is good. It it looked like it had found an optimal path to the solution very quickly. And I was thinking in my mind, well, maybe there's some natural pattern that that there's there's there's there's something about that that we could use in the abstract to guide the evolution in the future. But the other thing I'm thinking about is right now, the problem with machine learning is that we don't really have semantics baked in. So what we're doing is we have a verifier, we're looking at the rewards, and we're sort of like doing patterned exploration, and we're taking steps towards the, you know, towards the target. And I love mechanistic forms of reasoning where we actually know something about what the program components mean. And the reason this is important is when we're merging together the best performing programs from 2 different islands. That's a kind of first order interaction. It might not make sense to merge them together. It's wonderful that LLMs, you can give them any pairs of programs and it will find a way to merge them together. But wouldn't a more principled way be of there's there's some kind of semantic primitives here and we know they fit together. So there's this Lego analogy that we're kind of building up based on principles rather than forging our path based on the performance.…

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