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Speaker unverified: belief

27 Sept 2025 Machine Learning Street Talk New top score on ARC-AGI-2-pub (29.4%) - Jeremy Berman

“I think the fundamental reason why I got higher and also, I could match his efficiency, and I would still get higher because I was using natural language.”

— Speaker unverified

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Speaker
Speaker unverified
Attribution
Not verified from this transcript
Claim type
belief
Recorded
27 Sept 2025
Publisher
Machine Learning Street Talk

Transcript context

…0, okay. And I think Eric's 1 was slightly more efficient, and and I and he was It was. Indicating that it was because he was doing the library learning and transfer. And and even that, I'd I was left kind of thinking, first of all, it's interesting that you got better results. And is that because there isn't much transfer? You know, where where does the library transfer come into this? Because, know, like, maybe, like, the broader question is, if you were to make your solution significantly more efficient, what would you do? I had a version that does do library transfer. Basically, I would, save the traces from training and try and basically pull those in during test time. And, I actually, just out of simplicity's sake, because I was getting such high scores with the simple solution, I wanted to just push the simple solution, and I might actually it's we'll see. If someone's gonna beat my score, I might bring that back in. I it's for sure that will improve the score, and it's useful. There is a lot of transfer efficiency. I just found what I was doing very elegant, and so I I actually liked liked to keep it. But, you know, no third party dependencies or anything like that. But that for sure helps accuracy. I think the fundamental reason why I got higher and also, I could match his efficiency, and I would still get higher because I was using natural language. Natural language is a much more efficient area to play in. Yeah.…

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