The next big breakthrough will be AIs learning on the job

**Dwarkesh Patel** (0:00)
So, here's a big research bet that all the labs are making. They think that if we train AIs to accomplish millions of verifiable tasks across thousands of diverse RL environments, then we will have basically built AGI. Because this kind of training will have created a kind of problem-solving agent, the kind of thing that can make progress on open-ended tasks for weeks on end in the face of errors and mistakes and ambiguity. And the people who are optimistic about this vision will say that all these things that we talk about as the fundamental deficits in the current training paradigm, for example, the data inefficiency of these models or the fact that they lack into learning. These things can just be steamrolled if we just scale training more. And the same way that all the fundamental research problems in natural language processing collapsed when we just threw enough compute into LLMs.
So, in the previous essay, I talked about how these models are one one millionth as sample efficient as humans. And the people who are in favor of the current training paradigm will say, look, that might be true, but this is only true during training. And training is this one time cost that is amortized across billions of sessions that a model will experience. And what really matters is how smart and general and sample efficient the model is during a session. And this has clearly been improving as we have been doing more RL training. AI agents are able to solve more and more ambitious problems over longer and longer time spans. Anybody who has used these models for coding knows that. Similarly, people would say, look, continue learning, this capability I keep harping about where the model's weights get updated based on what it's learning from deployment may simply not be necessary. Because if in-context learning gets so good across longer and longer time horizons, then you don't need to distill back everything the model is learning on the job into the weights. People often say that their employees are not net productive until six months or more of them working on the job. So clearly, online learning is necessary for competence. But what if you could just fit those six months into the context window? There's been tons of architectural innovations that dramatically increase the amount of information or the amount of context that a transformer can store. And why not think with a couple more years of progress, you might have what feels like infinitely large context windows. Before we discuss this research a bit further, I want to step back and I want to ask a completely tangential question, which I find actually very interesting and confusing about the nature of current AI progress. Why has progress on computer use been so much slower than other domains? Computer use is so clearly verifiable. You could ask a question like, did the desired Etsy item I ordered get delivered? Is the venue for an event I'm trying to organize booked? Have my taxes been submitted? So isn't it weird that computer use has been making so much slower progress than coding and math and these other verifiable domains? I'm sure there's many reasons for this. And one of them, of course, is the fact that the modelers are exposed to far less high-quality multimodal data during pre-training. But one reason that I think is actually quite underrated by people, and which I think reveals the canyon walls against which this river of AI progress will only slowly chip away at, is that it is not enough for a domain to be verifiable. It also has to be very grindable, in the sense that you have to be able to run lots of parallel rollouts against a deterministic and replayable simulator, and you have to run those rollouts from the same starting point. If you're trying to make a model better at coding, you can define some container that has the software repo, it's a missing feature that you have tasked the AIs with creating. And you have a thousand parallel agents that go at the problem, each of which has an identical copy of the container. But this doesn't work with computer use, at least not trivially. You can't just have a thousand agents go try the same check out flow on Amazon to get better at using websites, because Andy Chassie will find your bots and shut your ass down.
You can solve this by making clones of Slack and Gmail and all the other common applications and websites.
But at least currently, this is a very labor intensive and unscalable way to build environments.
Of course, once AIs get good enough at coding themselves to build these clones with extremely high fidelity, then I'm sure the computer use will make quicker progress than it is right now. And you're also killing two birds with one stone with this kind of procedure, because getting AIs to rebuild whole applications from scratch is also a great RL objective for coding. So, while computer use itself may soon be solved, its current lethargy is telling us the following, that unless you can build a very replayable training target for a domain, the models will struggle to make much progress.