Pim de Witte
speaker
720 appearances
1 recordings
1 series
first heard Dec 2025
last heard 6 Dec
Pim de Witte’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Dec 2025 with 1.
Appearances
That to me was like one of the most interesting parts about the research.
Made me think about our own behaviors in a way.
Yeah.
Yeah, I started reading all the research papers.
By that time, I was already pretty deep into having a decent understanding of not world models, in particular LLMs and Transformer-based models.
And so there was Genie, there was Sima.
Those two were really, really interesting.
And Sima in particular was interesting because what they do is they basically take 10 games and then they...
they have a graphic in Sima where you can see kind of the precise actions that are inside of those games that they mapped.
And I believe they found something like 100, which are actually actions that also exist in the real world.
And what they did was they then, I believe it was specifically for navigation,
They did a 9-1 holdout set, so they trained an agent on the 9 games, and then they had it play the 10th game, the holdout game.
But then they also trained a specialized agent just on the 10th game, and they compared how good they did.
If I recall correctly, it did roughly as well playing the 10th game on navigation specifically on the holdout on the 9-game agent than it did on the 1-game agent.
And that to me was really interesting because that's precisely the type of data that we had, right?
And so for us, the thinking was, okay, what if we did exactly what LLMs did?
What if we use, right, this, right, so LLMs were trained on predicting like text tokens on words on the internet.
What if we predict action tokens on essentially what is the equivalent of the common crawl data set, but for interactivity?
Vision input.
Yeah, action output.
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