Ryan Greenblatt: evaluation

Transcript context

…What is your view on what is the least verifiable part of AI R&D? The least verifiable, probably making calls on large experiments. The thing that I think is most likely to be the bottleneck — in terms of the AIs being really good at verifiable domains but not at doing the actual thing — is just big experiments where you only get a few tries. Well, “a few” is maybe a bit understated. Historically R&D has been driven by doing near-frontier-scale experiments. That has been pretty important, actually doing the one big training run where you decide exactly what to include. There’s a bunch of ways that the AIs can make that more verifiable. They can have better science of exactly what to predict. They can scale down their frontier-scale training runs to a point where they can study that scale more aggressively, at some one-time hit to compute cost. If people wanted to, a thing you can always do is train smaller models so that you can run more rounds. I think we have seen this. One reason why the AIs have been scaled up less than you would have otherwise expected — and, for example, cost per token hasn’t increased as much as you might have thought — is because there is a benefit to doing more of your work at small scale, where you can run more training runs and get more cycles in. So you’re not leaning as hard on one big, really important training run. I just want to unpack a couple of things for the audience. The thing you’re pointing out is that the price per token has not increased that much since 2024 or 2023.…

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