I think you’re thinking about it in slightly the wrong way. We’re not throwing more data at frontier models in hopes they get more/better capabilities somehow.
replyWe’re either: setting up a verifiable task, and doing RLVR to get the model better at achieving that task.
Or we’re simply asking: “What do we want the model to do that it can’t now, and how do we curate data that would benefit it on that task?”
Most useful capabilities going forward aren’t going to come from data accidentally found on the net; that’s already all been scraped. You need to develop the dataset that shows how a model could perform insert task in its provided environment, and this still requires a decent bit of human ingenuity.
Yeah, there's room for improvement at every level, but your specific example: How do you get more and more difficult tasks where you can steer the training? To me, that seems limited by how creative humans can be. How do you get past AGI and into ASI with that? If the AIs make the tasks, how could we encourage them to be useful? Maybe you could push for harder and harder math proofs, but other than that I'm not sure.
replyAnyways, I'd be thrilled to see exponential (or faster) growth. Bring on the Culture, Accelerando, whatever. I just don't see it yet.