Sebastian Raschka
speaker
1,024 appearances
1 recordings
1 series
first heard Feb 2026
last heard 1 Feb
Sebastian Raschka’s voice in public audio — every appearance, attributed to the second.
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And right now, I think this year will be
more also the year figuring out like you said how to be more smart about that I think right now people want to have the next state of the art and the state of the art is happens to be the brute force expensive thing and then once you have that like you said keep that accuracy but let's see how we can do that cheaper now like tricks you know
Yes, I do think also with LLMs, what's an interesting thing here is I think if we unlock more LLM capabilities, it also automatically unlocks all the other fields because, or not unlocks, but like makes progress faster.
Because, you know, a lot of researchers and engineers use LLMs, like we said, for coding.
So even if they work on robotics, if you optimize these LLMs that help you with coding, you know, it's like it pays off.
But then, yes, world models are interesting.
It's basically where you have
the model run a simulation of the world, in a sense, like a little toy thing of the real thing, which can, again, unlock capabilities that the LLM is not aware of.
It can simulate things.
And I think, see, this is like something I think LLMs, they just happen to work
Well, by pre-training and then doing the next token prediction, but we could do this even a bit, you know, like sophisticated in a sense.
So what I'm saying is like with, there's like, I think it was by Meta, a paper, Coda World Models.
so where they basically apply the concept of models to llms again where they and so instead of just having next token prediction and verifiable rewards checking the answer correctness they also make sure the intermediate variables are correct you know like it's kind of like a the model is learning basically a code environment in a sense and i think this makes a lot of sense it's just like expensive to do but this is like making things more sophisticated like
modeling the whole thing, not just the result.
So it can add more value.
I remember when I was a grad student, there's a competition called CASP, I think, where they do protein structure prediction.
They predict the structure of a protein that is not solved yet at that point.
So in a sense, this is actually great.
And I think we need something like that for LLMs also where you do the benchmark, but no one does.
So you hand in the results, but no one knows the solution.
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