Ryan Greenblatt: belief

Transcript context

…Mythos took way more compute than they had back then, but even with the level of compute they had back then, not only do all the breakthroughs happen, but they also train Mythos with that level of compute. What would be required is obviously discovering all the algorithmic progress since then. It’s discovering even more, actually, because you’ve got to make up for the fact that Mythos uses… What was GPT-3 trained on? Like 1e23? We can look it up. But is it plausibly four orders of magnitude more compute? I think it’s somewhat less than that. Let’s look this up quickly. GPT-3 training compute is about 3e23. My sense is that Mythos is probably a little over three OOMs higher. So the question is: can you overcome this 1000x compute gap while also being the model? Here’s a concrete claim that maybe we should talk about. Right now, would we be able to train a model with GPT-3-level compute that matches… What exactly do I think? GPT-3 was released in 2020, so it was trained about six and a half, seven years ago. It’s worth noting that GPT-3 is maybe a little too far in the past, but let’s go with this for a second. If we were to train a model with GPT-3-level compute today, how good would that model be? My understanding, based on how algorithmic progress works, is that we’d be able to train a model that’s as good as the best model we had perhaps around three years ago. So I think that right now we’d be able to train a version of GPT-3 that’s probably somewhat better than GPT-4, a moderate amount better than GPT-4. I think that’s about right. That roughly lines up with how algorithmic progress has worked. Basically, the story would end up being that to get five years of AI progress, you’re probably going to need around, I would say, maybe eight years of algorithmic progress, very roughly, which is a lot of algorithmic progress. But it just turns out that most of the AI progress, from my perspective, has come from some mix of algorithms and data, and you can just keep making huge improvements on these things and training AIs with less compute. I’m glad you brought that up, because what has happened since GPT-3, or even 3.5, till now? Why is Mythos so good? Obviously, we’ve scaled the compute. We have better algorithms. But a huge thing that’s happened is that we have built a deca-billion-dollar data industry which has systematically collected and codified expert human judgment across all kinds of different disciplines — codified in the form of RL environments, codified in the form of SFT traces — that these experts built to help the model better understand how you do coding, how you build complex infrastructure projects, how you do law, how you do whatever. How are the AIs able to replicate the effect that expert human judgment currently seems to be playing in AI progress?…

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