Ryan Greenblatt: evaluation

Transcript context

…But how do you explain why the AIs have gotten so good at coding? I feel like a big part of that is data and RL environments, which are codifying human experts. But the question is what is the limiting factor on creating RL environments? My sense is that the reason why RL environments today are much better than they were in 2024 is not so much because we have hired way more human experts to make RL environments. It is instead much more because we better know what RL environments we even want to make and how we should structure them. Also, we’re using huge amounts of AI labor to build RL environments. I think those effects are much more important than the effect of human labor building the RL environments. I’m not saying that the human labor doesn’t matter. I’m just saying there are other big drivers that are important here. I could try to argue for this. One thing is just that the amount of environments people want is a very large amount. I think the AIs are actually pretty good at the task of making RL environments given some sense of what the thing should be. There’s preexisting data you could use. A lot of these things have good verification loops. Just look at, for example, what was reported in Business Insider yesterday, that Google is paying close to $2 billion for Mechanize. We can just look at market rates for what people think really good human expert data is worth. The frontier labs seem to think it’s worth a lot. They’re willing to pay for it.…

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