AI researchers debate how close we are to recursive self-improvement
New episode with John Schulman, Beren Millidge and Charlie O’Neill.
I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what’s actually happening at the frontier and what comes next.
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(00:00:00) – Steelmanning the case against RSI
(00:18:39) – What’s driving the Chinese labs’ progress
(00:28:06) – How will automated AI researchers be trained
(00:33:51) – Will long-horizon RL elicit AGI?
(00:45:24) – The sim-to-real gap
(01:00:33) – How much progress is explained by data?
(01:18:03) – Why is RL working so well?
(01:24:54) – Move 37 and entropy collapse
(01:28:32) – Rapid-fire timelines
Today, I’m chatting with three of my AI researcher friends from whom I learn a lot every time we talk. They also happen to be at somewhat open-ish labs and companies, so you guys can actually say things on the record. I’m joined by Beren Millidge, who is the CTO of Zyphra, which is developing open source models. John Schulman is the chief scientist at Thinking Machines, previously a co-founder of OpenAI, and led the RLHF work that led to ChatGPT. And Charlie O’Neill is head of model training at Baseten.
The first question I have: If we’re in 2036 and we don’t have billions of crazy superintelligences running around that have radically transformed the world, what is the most likely reason that doesn’t end up being the case? Other than exogenous political shocks, or there’s a war, or they ban AI or something. What is the most likely technical reason that 2036 isn’t a crazy alien superintelligence world?
There’s been a classic thing, almost like Moravec’s paradox, where we think of the AI as, “If it can do this, it’s going to be amazing.” If it can solve these hard maths problems, if it can win at chess, blah, blah, blah… Then it solves these things, and it’s not that impactful. Obviously, it’s somewhat impactful, but not everything.
If somehow that continues, and there’s never the true spark of generalization that occurs, I think that could lead to the AI just being extremely good at everything that people put into a benchmark or put into an environment. But there’s still some persistent sim-to-real gap which is somehow blocking everything. I think this is unlikely. We do actually see this kind of generalization even from RL in practice already. But if it is just ridiculously hard to generalize meta-learning, plus we don’t solve continual learning and it’s just super hard and impossible… This would be my default scenario in that case.
I agree with that. Humans have a lot of advantages over models now. Each time a new model comes out, it’ll catch up in some of these areas. But you end up getting bottlenecked by the places where the model is weaker and where it has worse judgment, or the models can’t check themselves well enough.
There’s this cycle that keeps repeating where a new model comes out and people are blown away and they’re like, “This is it. This is AGI.” But then they use it a bit, and it starts to feel dumb after a month or so. That cycle just might keep going. It’s hard to predict how many times it’s going to repeat.
Right now, you don’t get explosive growth in capabilities because you still get bottlenecked enough when you’re trying to do research and engineering. Even if the model can write way more code than a person, it doesn’t make you 100X more productive. So maybe there are just more of these cycles than we would expect.
For me, it’s a question of how far off the global optimum of “a learner you could have on a chip” is from the transformer + RL, basically the current recipe. People imagine that once you have an agent which is better than all humans at AI research, even if it’s 0.1% better than all humans, then the fact that you can run hundreds of thousands, if not millions, of these in parallel — and you can run them much faster as chips speed up — is going to outweigh every other bottleneck. You’re eventually going to hit this very fast takeoff with regards to self-improvement.
I could imagine that if we continue along the trajectory that we’re currently on with that paradigm, where it’s basically self-attention, RL, scaling up RL environments… Think about what happened with Moore’s law. We had this very nice straight line and that held for a really, really long time. But there were so many discrete discontinuities and innovations that had to happen to keep that scaling law going. The same thing has happened with LLMs. We had this pre-training scaling law, and then that was hitting diminishing returns. Then we came up with RL and solved that, and then we got this new diminishing returns curve to hit that made it keep looking like a straight line going up.
So if it requires another one of those discontinuities to solve, I’m not sure that the current method of training LLMs with these RL environments, even RSI-targeted RL environments, would be able to discover that discontinuity. If not, we’re probably going to hit this asymptotic curve.
But do you think the discontinuity will be harder than anything that’s come since 2012?
If we had the answer to that, we’d kind of have the ability to implement it. But maybe we should distinguish between a discontinuity which adds to the current paradigm, which is cumulative — there’s something beyond the RL that we have to discover, and maybe they’re capable of connecting the dots in that straight line — or, again, how far off the global optimum are we? Do we have to go back and throw out gradient descent and neural nets in general? I don’t think, if you continue to scale up the current paradigm, an LLM, no matter how many LLMs you’re running, is necessarily capable of discovering that if it’s too far away.
The only hope really is if deep learning just can’t get us to an AI which can at least dominate human research and human development, including the human ability to come up with new paradigms and so forth. Or, I don’t know, maybe humans would also never have discovered the next learning architecture. But to the extent humans could have discovered it eventually… But it just seems like… If you just look at the progress that’s happened since 2012 till now, and you just continue that on —I know it’s just been powered by huge amounts of compute scaling and so forth— it would be weird if it just didn’t get to the point where it could dominate humans, at least in R&D, especially over the next few years.
Ryan Greenblatt was on the podcast recently. He made this point that I’d be curious to get your thoughts on. You could imagine, as AIs get more and more capable, that they’re capable of making progress on simulations which incentivize getting better at not only AI R&D, but at science generally. This is a thing that all the labs are targeting and many startups are targeting.
Another intuition pump is if you look at the Elo score of chess bots since the ’80s. There’s a very linear increase in Elo over time. But there’s this huge discontinuity as they cross the human range, from human experts always winning against AIs to human experts never winning against AIs, as this linear increase in Elo happens.
I agree with your point that so far, AI capabilities have not been that big of a deal in terms of their end economic impact in the world. But that is because they’re slowly rising in Elo relative to humans.
I agree it would be very surprising. The only way for this to not happen is if, as you said, it somehow asymptotes just before. Because we’re already pretty close, in my opinion, to where we’ll start crossing the human Elo score. So we’ll need to asymptote before that. That’s the only way — in this scenario you pose where somehow we’re sitting here in 2035 and everything is normal — for this to happen, I think. The only other way is there’s some dramatic regulation on AI. This is what I see as the most likely way for this scenario to happen, actually, rather than a technical thing.
I think there’s different kinds of research. There’s research in the autoresearch style where the objective is already specified very cleanly and you’re optimizing that objective. I think everyone is picturing that if we continue along this path of making pre-training loss go down and making our environments have the reward on them go up, that’s going to lead to improvement.
But maybe what Ryan is talking about is this much more open-ended type of science which is required for paradigm shifts, where we can’t specify the objective, and the AIs are definitely not able to specify that objective either. We have to be really, really careful about how we specify objectives for any of these things.
Maybe your point is that the nature of the breakthroughs that have happened since 2012 is that we have found… In 2012, people weren’t saying… I’m assuming, I don’t know, you guys were there. Or at least John, you were there. But I was not.
I was in primary school.
Actually, John, I’m curious for your wisdom of the ages, or wisdom of being in the trenches way back when. Presumably, a big breakthrough was realizing that next token prediction is the… You wouldn’t have thought that the nanoGPT speed run is the thing to be optimizing for in 2014. But now that we have come to this new paradigm, you would think to do a speed run on that and have AIs get really good at that.
But maybe there’s a next inner loop to optimize that the AIs wouldn’t anticipate. There’s an outer loop of revenue or something that eventually should be strong, but it’s a very slow outer loop.
In fact, I remember in the early OpenAI days having the intuition that just minimizing log loss wasn’t going to get you to intelligence. Because the important bits are accounting for such a small fraction of the loss that it was going to be overwhelmed by noise. So just training a language model on next token prediction wasn’t going to learn the interesting things you want it to learn. We needed to craft better objectives that would put more emphasis on the important things.
You can make all sorts of arguments for this. You could say, “Oh, humans probably don’t learn how to model everything in our environment. Most people can’t create a photorealistic reproduction of some kind of scene they’ve looked at. So we must need a better objective.” But then it turned out that it just worked anyway.
As you were pointing out, the inner loop, even in current AI research, of post-training benchmarks or whatever, doesn’t necessarily translate into what users like.
Oh, yeah. The whole field relies a lot on generalization and it’s very hard to predict when you’re going to get generalization, or when you’re going to get some kind of out-of-distribution generalization. We know that if you train on the task you care about, you’re going to do better. But the most important advances are often types of generalization that we have no right to expect.
For example, from just pre-training on this very naive next-token-prediction objective to various tasks of interest that require understanding of the input in some deep way, or learning some skill from pre-training that’s very rare and not heavily represented. Then also generalization from these verifiable tasks to less verifiable ones, this is also a type of generalization that there’s no reason a priori to expect.
This is an interestin