Nick Joseph, Head of Pre-Training at Anthropic, describes the economic engine driving continuous pre-training model scale over the past five years.
Insight
Anthropic's Joseph: Compute matters far more than pre-training objective details
“I think that, like, the one sort of general intuition I have is, like, compute is the thing that matters. So, like, I think if you throw enough compute at any of these objectives, you're gonna get something that's probably pretty good, and can kind of be fine …”
Insight
Joseph: LLM training requires collaborative infrastructure work over publishable research papers
“And to do a project like training a large language model requires a lot of people to collaborate on like a really complicated piece of infrastructure that isn't going to be a paper, right? Like you're not going to publish like, oh, I got a slightly, I got five…”
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Joseph: Sparsely linked long-tail data may be most valuable for frontier AI
“And it might be that like, that data ends up more valuable because you, everything that's linked to a lot, you've already got. Like at some point, you're maybe like going for the tails, or you're going for the stuff that no one's ever, like, you know, it's onl…”
Insight
Joseph: Training purely on raw LLM generations cannot produce a better model
“Theoretically, I shouldn't be able to train a better model than that. Like, I'm just going to get the same thing out. So I think that's-”
Insight
Nick Joseph: Third parties can steer frontier AI labs by publishing evals
“Like, it is the case that, like, the labs right now are really driven by getting good eval scores. And it's hard to make them, and anyone can do it. There's no comparative advantage to having the model to making an eval. So I do think it's actually, like, an i…”
Prediction Not checkable as stated
Anthropic's Joseph: Certain AI Alignment Pieces Will Move to Pre-Training
“I do think at some point there will be, like, some pieces of alignment that, like, you do want to export back into pre-training because that might be a way to, like, Put them in with more strength, like, more robustness, kind of, or more core to the intelligen…”