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Chapter 1: What big research bet are AI labs making?
So here's the 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.
Chapter 2: Why is grindability important in AI training?
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 new 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.
Chapter 3: Can RLVR alone lead to general AI?
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.
Chapter 4: How does learning get integrated back into AI models?
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've 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.
Chapter 5: What might continual learning look like in 2027?
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. Okay, so 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.
Chapter 6: Why is computer use progress slower than coding?
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.
Chapter 7: How do we train AI for real-world applications?
And one of them, of course, is the fact that the models 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 with some missing feature that you have tasked the AIs with creating.
And then 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 checkout flow on Amazon to get better at using websites because Andy Jassy 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. And the reason this is true, of course, the models are incredibly sample inefficient during training.
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Chapter 8: What is the future of AI with continual learning?
This is a point I was making in my last video essay. So for computer use, we might be able to make up for the sample efficiency deficit by building these farmable deterministic simulators. But for so many other different kinds of skills that we need AIs to have, we simply can't do this. How do we train an AI to get really good at building a business from scratch?
How about winning court cases, or having a profitable day of trading in the markets, or helping a candidate win an election? The rollout here requires interacting with the real world, and you can't recreate it from just within the data center.
The outer-loop verification here may take months or even years of real-world actions to elicit, and you can't re-observe it by perturbing the model's actions slightly in thousands of parallel rollouts to isolate exactly what the model did that actually worked. Now, dealing with such reset-free non-stationary environments is a known open problem in RL.
I'm not pointing out anything new, but I really do want to emphasize that because of the idiosyncratic and sparse nature of data in most domains in the world, you need sample efficiency in order to get proficient. If AIs are to develop all the skills that humans have,
and even skills that humans don't have, then they need to be able to learn from information revealed in unstructured, unverifiable, and ambiguous ways from scarce amounts of real-world interaction. Because in many domains, the relevant training information simply doesn't exist in any other way.
What is the RL environment to make an AI that is as good at politics as Lyndon Johnson, or as good at building a space launch business as Elon Musk? The labs are betting that RLVR will generalize.
That is that if you train on enough containerized reproducible environments, you will develop a very general agent that can make and execute plans and learn rapidly from new information and even pick up new skills all within a single session. If you drop this endlessly RLVR'd AI into Texas politics in 1948, it could give you better advice than LBJ about winning the Senate seat.
And if you give it $100 million in 2002 and let it cook, it would build SpaceX for you. Now whether RLVR can generalize this well is an empirical question. If the labs went from spending billions of dollars on RL environments to a trillion dollars, would you get the kind of thing that is a fully human-like general intelligence within the context window?
Dario gave a telling quote during our podcast together, which I think hints that our LVR generalization is not infinitely strong.
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