Some thoughts on the Sutton interview

Boy, do you guys have a lot of thoughts about this on an interview? I've been thinking about it myself, and I think I have a much better understanding now of Sutton's perspective than I did during the interview itself. So I wanted to reflect on how I understand his worldview now. And, Richard, apologies if there are still any errors or misunderstandings. It's been very productive to learn from your thoughts. Okay. So here's my understanding of the steel man of Richard's position. Obviously, he wrote the same essay, The Bitter Lesson. And what is this essay about? Well, it's not saying that you just wanna throw away as much compute as you possibly can. The bitter lesson says that you want to come up with techniques which most effectively and scalably leverage compute. Most of the compute that's spent on an LLM is used in running it during deployment, and yet it's not learning anything during this entire period. It's only learning during this special phase that we call training. And so this is obviously not an effective use of compute. And what's even worse is that this training period by itself is highly inefficient because these models are usually trained on the equivalent of tens of thousands of years of human experience. And what's more, during this training phase, all of their learning is coming straight from human data. Now this is an obvious point in the case of free training data, but it's even kind of true for the RLVR that we do with these LLMs. These RL environments are human furnished playgrounds to teach LLMs the specific skills that we have prescribed for them. The agent is in no substantial way learning from organic and self directed engagement with the world. Having to learn only from human data, which is an inelastic and hard to skill resource, is not a scalable way to use compute. Furthermore, what these LLMs learn from training is not a true world model, which would tell you how the environment changes in response to different actions that you take. Rather, they're building a model of what a human would say next. And this leads them to rely on human derived concepts. A way to think about this would be, suppose you trained an LLM on all the data up to the year 1900. That LLM probably wouldn't be able to come up with relativity from scratch. And maybe here's a more fundamental reason to think this whole paradigm will eventually be superseded. LLMs aren't capable of learning on the job, so we'll need some new architecture to enable this kind of continual learning. And once we do have this architecture, we won't need a special trading face. The agents will just be able to learn on the fly like all humans and, in fact, like all animals are able to do. And this new paradigm will render our current approach with LLMs and their special training phase that's super sample …