The next big breakthrough will be AIs learning on the job
Here’s the big research bet the labs are making currently: if we train AIs to accomplish millions of verifiable tasks across thousands of diverse RL environments, then we’ll basically have built AGI. Because such training will create these general problem solving skills (like how to make progress on an open ended task for weeks on end in the face of errors, mistakes, and ambiguity).
The people optimistic about this vision would say that anything we might consider a fundamental deficits with the current learning paradigm—for example, data inefficiency and lack of continual learning—can be steamrolled by just scaling training more, just as all the supposed “fundamental” research problems in natural language processing collapsed against the flood of compute thrown into LLMs.
Yes, these models are1/1-millionth as sample efficient as humans during training. But training a one-time cost amortized across billions of user sessions. What matters is how smart, general, and sample efficient the model is within a session, and that’s clearly been improving as we do more RL training. AIs are able to solve more and more ambitious problems across longer and longer time spans - anybody who’s been using these models for coding knows that.
Similarly, continual learning—as defined as the model’s weights getting updated from deployment—may simply not be necessary. Again, because if in-context learning gets so good across longer and longer horizons, then we don’t need to distill back to weights to get on-the-job learning. People often say that their employees are not net productive until six months or more 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 on the transformer which dramatically increase the length of context you can store. With a couple more years of progress, why couldn’t we have arbitrarily large context windows?
Grindability is just as important as verifiability
To address whether this will work, I want to first take a detour and ask a question about the current nature of AI progress that I find confusing and interesting. Why has progress on computer use been so slow?
Computer use is so clearly verifiable (did the desired Etsy item get ordered, is everything I need corporate for my event booked, have my taxes been submitted). So isn’t it weird that computer use has been making much slower progress than coding and math and other verifiable domains? There’s many reasons for this, I’m sure, among them the fact that the models are exposed to far less high quality multimodal data during pretraining, and that video consumes the context window far faster.[1]
But one reason that I think it quite underrated, and also which reveals the canyon walls against which the 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 can run lots of parallel rollouts against a deterministic and replayable simulator. If you’re trying to make a model better at coding, you can create an environment that has a software repo with some missing feature that you’ve tasked the AIs with creating, and then you have a thousand parallel agents just go at the problem, each with their identical copy of the container.[2]
But this doesn’t work with computer use—at least not trivially. You can’t have a thousand agents go try the same checkout flow on Amazon.com. Because Andy Jassy will find and detect your bots and shut your ass down.
You can solve this by making clones of Slack, 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 to themselves build these clones with extremely high fidelity, then I’m sure computer use wi
