Mike's Notes
A fascinating interview with Richard Sutton. I agree with what Richard is saying.
Resources
- dwarkesh.com/p/richard-sutton
- https://www.youtube.com/watch?v=21EYKqUsPfg
- https://en.wikipedia.org/wiki/Richard_S._Sutton
- https://en.wikipedia.org/wiki/Bitter_lesson
- http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- https://en.wikipedia.org/wiki/Reinforcement_learning
- https://en.wikipedia.org/wiki/Temporal_difference_learning
- https://en.wikipedia.org/wiki/Markov_decision_process
References
- The Bitter Lesson by Richard Sutton, 2019.
Repository
- Home > Ajabbi Research > Library >
- Home > Handbook >
Last Updated
30/01/2026
Richard Sutton – Father of RL thinks LLMs are a dead end
Host of Dwarkesh Podcast.
Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end.
After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need some new architecture to enable continual learning.
And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals.
This new paradigm will render our current approach with LLMs obsolete.
In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew.
A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment.
Enjoy!
Watch on YouTube; listen on Apple Podcasts or Spotify.
Timestamps
- (00:00:00) – Are LLMs a dead end?
- (00:13:04) – Do humans do imitation learning?
- (00:23:10) – The Era of Experience
- (00:33:39) – Current architectures generalize poorly out of distribution
- (00:41:29) – Surprises in the AI field
- (00:46:41) – Will The Bitter Lesson still apply after AGI?
- (00:53:48) – Succession to AI
Transcript
00:00:00 – Are LLMs a dead end?
Dwarkesh Patel 00:00:00
Today I’m chatting with Richard Sutton, who is one of the founding fathers of reinforcement learning and inventor of many of the main techniques used there, like TD learning and policy gradient methods. For that, he received this year’s Turing Award which, if you don’t know, is the Nobel Prize for computer science. Richard, congratulations.
Richard Sutton 00:00:17
Thank you, Dwarkesh.
Dwarkesh Patel 00:00:18
Thanks for coming on the podcast.
Richard Sutton 00:00:20
It’s my pleasure.
Dwarkesh Patel 00:00:21
First question. My audience and I are familiar with the LLM way of thinking about AI. Conceptually, what are we missing in terms of thinking about AI from the RL perspective?
Richard Sutton 00:00:33
It’s really quite a different point of view. It can easily get separated and lose the ability to talk to each other. Large language models have become such a big thing, generative AI in general a big thing. Our field is subject to bandwagons and fashions, so we lose track of the basic things. I consider reinforcement learning to be basic AI.
What is intelligence? The problem is to understand your world. Reinforcement learning is about understanding your world, whereas large language models are about mimicking people, doing what people say you should do. They’re not about figuring out what to do.
Dwarkesh Patel 00:01:19
You would think that to emulate the trillions of tokens in the corpus of Internet text, you would have to build a world model. In fact, these models do seem to have very robust world models. They’re the best world models we’ve made to date in AI, right? What do you think is missing?
Richard Sutton 00:01:38
I would disagree with most of the things you just said. To mimic what people say is not really to build a model of the world at all. You’re mimicking things that have a model of the world: people. I don’t want to approach the question in an adversarial way, but I would question the idea that they have a world model. A world model would enable you to predict what would happen. They have the ability to predict what a person would say. They don’t have the ability to predict what will happen.
What we want, to quote Alan Turing, is a machine that can learn from experience, where experience is the things that actually happen in your life. You do things, you see what happens, and that’s what you learn from. The large language models learn from something else. They learn from “here’s a situation, and here’s what a person did”. Implicitly, the suggestion is you should do what the person did.
Dwarkesh Patel 00:02:39
I guess maybe the crux, and I’m curious if you disagree with this, is that some people will say that imitation learning has given us a good prior, or given these models a good prior, of reasonable ways to approach problems. As we move towards the era of experience, as you call it, this prior is going to be the basis on which we teach these models from experience, because this gives them the opportunity to get answers right some of the time. Then on this, you can train them on experience. Do you agree with that perspective?
Richard Sutton 00:03:12
No. I agree that it’s the large language model perspective. I don’t think it’s a good perspective. To be a prior for something, there has to be a real thing. A prior bit of knowledge should be the basis for actual knowledge. What is actual knowledge? There’s no definition of actual knowledge in that large-language framework. What makes an action a good action to take?
You recognize the need for continual learning. If you need to learn continually, continually means learning during the normal interaction with the world. There must be some way during the normal interaction to tell what’s right. Is there any way to tell in the large language model setup what’s the right thing to say? You will say something and you will not get feedback about what the right thing to say is, because there’s no definition of what the right thing to say is. There’s no goal. If there’s no goal, then there’s one thing to say, another thing to say. There’s no right thing to say.
There’s no ground truth. You can’t have prior knowledge if you don’t have ground truth, because the prior knowledge is supposed to be a hint or an initial belief about what the truth is. There isn’t any truth. There’s no right thing to say. In reinforcement learning, there is a right thing to say, a right thing to do, because the right thing to do is the thing that gets you reward.
We have a definition of what’s the right thing to do, so we can have prior knowledge or knowledge provided by people about what the right thing to do is. Then we can check it to see, because we have a definition of what the actual right thing to do is.
An even simpler case is when you’re trying to make a model of the world. When you predict what will happen, you predict and then you see what happens. There’s ground truth. There’s no ground truth in large language models because you don’t have a prediction about what will happen next. If you say something in your conversation, the large language models have no prediction about what the person will say in response to that or what the response will be.
Dwarkesh Patel 00:05:29
I think they do. You can literally ask them, “What would you anticipate a user might say in response?” They’ll have a prediction.
Richard Sutton 00:05:37
No, they will respond to that question right. But they have no prediction in the substantive sense that they won’t be surprised by what happens. If something happens that isn’t what you might say they predicted, they will not change because an unexpected thing has happened. To learn that, they’d have to make an adjustment.
Dwarkesh Patel 00:05:56
I think a capability like this does exist in context. It’s interesting to watch a model do chain of thought. Suppose it’s trying to solve a math problem. It’ll say, “Okay, I’m going to approach this problem using this approach first.” It’ll write this out and be like, “Oh wait, I just realized this is the wrong conceptual way to approach the problem. I’m going to restart with another approach.”
That flexibility does exist in context, right? Do you have something else in mind or do you just think that you need to extend this capability across longer horizons?
Richard Sutton 00:06:28
I’m just saying they don’t have in any meaningful sense a prediction of what will happen next. They will not be surprised by what happens next. They’ll not make any changes if something happens, based on what happens.
Dwarkesh Patel 00:06:41
Isn’t that literally what next token prediction is? Prediction about what’s next and then updating on the surprise?
Richard Sutton 00:06:47
The next token is what they should say, what the actions should be. It’s not what the world will give them in response to what they do.
Let’s go back to their lack of a goal. For me, having a goal is the essence of intelligence. Something is intelligent if it can achieve goals. I like John McCarthy’s definition that intelligence is the computational part of the ability to achieve goals. You have to have goals or you’re just a behaving system. You’re not anything special, you’re not intelligent. You agree that large language models don’t have goals?
Dwarkesh Patel 00:07:25
No, they have a goal.
Richard Sutton 00:07:26
What’s the goal?
Dwarkesh Patel 00:07:27
Next token prediction.
Richard Sutton 00:07:29
That’s not a goal. It doesn’t change the world. Tokens come at you, and if you predict them, you don’t influence them.
Dwarkesh Patel 00:07:39
Oh yeah. It’s not a goal about the external world.
Richard Sutton 00:07:43
It’s not a goal. It’s not a substantive goal. You can’t look at a system and say it has a goal if it’s just sitting there predicting and being happy with itself that it’s predicting accurately.
Dwarkesh Patel 00:07:55
The bigger question I want to understand is why you don’t think doing RL on top of LLMs is a productive direction. We seem to be able to give these models the goal of solving difficult math problems. They are in many ways at the very peaks of human-level in the capacity to solve math Olympiad-type problems. They got gold at IMO.
So it seems like the model which got gold at the International Math Olympiad does have the goal of getting math problems right. Why can’t we extend this to different domains?
Richard Sutton 00:08:27
The math problems are different. Making a model of the physical world and carrying out the consequences of mathematical assumptions or operations, those are very different things. The empirical world has to be learned. You have to learn the consequences. Whereas the math is more computational, it’s more like standard planning. There they can have a goal to find the proof, and they are in some way given that goal to find the proof.
Dwarkesh Patel 00:09:10
It’s interesting because you wrote this essay in 2019 titled “The Bitter Lesson,” and this is the most influential essay, perhaps, in the history of AI. But people have used that as a justification for scaling up LLMs because, in their view, this is the one scalable way we have found to pour ungodly amounts of compute into learning about the world. It’s interesting that your perspective is that the LLMs are not “bitter lesson”-pilled.
Richard Sutton 00:09:41
It’s an interesting question whether large language models are a case of the bitter lesson. They are clearly a way of using massive computation, things that will scale with computation up to the limits of the Internet. But they’re also a way of putting in lots of human knowledge. This is an interesting question. It’s a sociological or industry question. Will they reach the limits of the data and be superseded by things that can get more data just from experience rather than from people?
In some ways it’s a classic case of the bitter lesson. The more human knowledge we put into the large language models, the better they can do. So it feels good. Yet, I expect there to be systems that can learn from experience. Which could perform much better and be much more scalable. In which case, it will be another instance of the bitter lesson, that the things that used human knowledge were eventually superseded by things that just trained from experience and computation.
Dwarkesh Patel 00:11:17
I guess that doesn’t seem like the crux to me. I think those people would also agree that the overwhelming amount of compute in the future will come from learning from experience. They just think that the scaffold or the basis of that, the thing you’ll start with in order to pour in the compute to do this future experiential learning or on-the-job learning, will be LLMs.
I still don’t understand why this is the wrong starting point altogether. Why do we need a whole new architecture to begin doing experiential, continual learning? Why can’t we start with LLMs to do that?
Richard Sutton 00:11:58
In every case of the bitter lesson you could start with human knowledge and then do the scalable things. That’s always the case. There’s never any reason why that has to be bad. But in fact, and in practice, it has always turned out to be bad. People get locked into the human knowledge approach, and they psychologically… Now I’m speculating why it is, but this is what has always happened. They get their lunch eaten by the methods that are truly scalable.
Dwarkesh Patel 00:12:34
Give me a sense of what the scalable method is.
Richard Sutton 00:12:37
The scalable method is you learn from experience. You try things, you see what works. No one has to tell you. First of all, you have a goal. Without a goal, there’s no sense of right or wrong or better or worse. Large language models are trying to get by without having a goal or a sense of better or worse. That’s just exactly starting in the wrong place.
00:13:04 – Do humans do imitation learning?
Dwarkesh Patel 00:13:04
Maybe it’s interesting to compare this to humans. In both the case of learning from imitation versus experience and on the question of goals, I think there’s some interesting analogies. Kids will initially learn from imitation. You don’t think so?
Richard Sutton 00:13:24
No, of course not.
Dwarkesh Patel 00:13:27
Really? I think kids just watch people. They try to say the same words…
Richard Sutton 00:13:32
How old are these kids? What about the first six months?
Dwarkesh Patel 00:13:37
I think they’re imitating things. They’re trying to make their mouth sound the way they see their mother’s mouth sound. Then they’ll say the same words without understanding what they mean. As they get older, the complexity of the imitation they do increases. You’re imitating maybe the skills that people in your band are using to hunt down the deer or something. Then you go into the learning from experience RL regime. But I think there’s a lot of imitation learning happening with humans.
Richard Sutton 00:14:04
It’s surprising you can have such a different point of view. When I see kids, I see kids just trying things and waving their hands around and moving their eyes around. There’s no imitation for how they move their eyes around or even the sounds they make. They may want to create the same sounds, but the actions, the thing that the infant actually does, there’s no targets for that. There are no examples for that.
Dwarkesh Patel 00:14:37
I agree. That doesn’t explain everything infants do, but I think it guides a learning process. Even an LLM, when it’s trying to predict the next token early in training, it will make a guess. It’ll be different from what it actually sees. In some sense, it’s very short-horizon RL, where it’s making this guess, “I think this token will be this.” It’s this other thing, similar to how a kid will try to say a word. It comes out wrong.
Richard Sutton 00:14:58
The large language models are learning from training data. It’s not learning from experience. It’s learning from something that will never be available during its normal life. There’s never any training data that says you should do this action in normal life.
Dwarkesh Patel 00:15:15
I think this is more of a semantic distinction. What do you call school? Is that not training data?
Richard Sutton 00:15:22
School is much later. Okay, I shouldn’t have said never. I don’t know, I think I would even say that about school. But formal schooling is the exception. You shouldn’t base your theories on that.
Dwarkesh Patel 00:15:35
But there are phases of learning where there’s the programming in your biology early on, you’re not that useful. Then why you exist is to understand the world and learn how to interact with it. It seems like a training phase. I agree that then there’s a more gradual… There’s not a sharp cutoff to training to deployment, but there seems to be this initial training phase right?
Richard Sutton 00:15:59
There’s nothing where you have training of what you should do. There’s nothing. You see things that happen. You’re not told what to do. Don’t be difficult. I mean this is obvious.
Dwarkesh Patel 00:16:14
You’re literally taught what to do. This is where the word training comes from, from humans.
Richard Sutton 00:16:20
I don’t think learning is really about training. I think learning is about learning, it’s about an active process. The child tries things and sees what happens. We don’t think about training when we think of an infant growing up.
These things are actually rather well understood. If you look at how psychologists think about learning, there’s nothing like imitation. Maybe there are some extreme cases where humans might do that or appear to do that, but there’s no basic animal learning process called imitation. There are basic animal learning processes for prediction and for trial-and-error control.
It’s really interesting how sometimes the hardest things to see are the obvious ones. It’s obvious—if you look at animals and how they learn, and you look at psychology and our theories of them—that supervised learning is not part of the way animals learn. We don’t have examples of desired behavior. What we have are examples of things that happen, one thing that followed another. We have examples of, “We did something and there were consequences.” But there are no examples of supervised learning.
Supervised learning is not something that happens in nature. Even if that were the case with school, we should forget about it because that’s some special thing that happens in people. It doesn’t happen broadly in nature. Squirrels don’t go to school. Squirrels can learn all about the world. It’s absolutely obvious, I would say, that supervised learning doesn’t happen in animals.
Dwarkesh Patel 00:18:11
I interviewed this psychologist and anthropologist, Joseph Henrich, who has done work about cultural evolution, basically what distinguishes humans and how humans pick up knowledge.
Richard Sutton 00:18:26
Why are you trying to distinguish humans? Humans are animals. What we have in common is more interesting. What distinguishes us, we should be paying less attention to.
Dwarkesh Patel 00:18:38
We’re trying to replicate intelligence. If you want to understand what it is that enables humans to go to the moon or to build semiconductors, I think the thing we want to understand is what makes that happen. No animal can go to the moon or make semiconductors. We want to understand what makes humans special.
Richard Sutton 00:18:54
I like the way you consider that obvious, because I consider the opposite obvious. We have to understand how we are animals. If we understood a squirrel, I think we’d be almost all the way there to understanding human intelligence. The language part is just a small veneer on the surface.
This is great. We’re finding out the very different ways that we’re thinking. We’re not arguing. We’re trying to share our different ways of thinking with each other.
Dwarkesh Patel 00:19:29
I think argument is useful. I do want to complete this thought. Joseph Henrich has this interesting theory about a lot of the skills that humans have had to master in order to be successful. We’re not talking about the last thousand years or the last 10,000 years, but hundreds of thousands of years. The world is really complicated.
It’s not possible to reason through how to, let’s say, hunt a seal if you’re living in the Arctic. There’s this many, many-step, long process of how to make the bait and how to find the seal, and then how to process the food in a way that makes sure you won’t get poisoned. It’s not possible to reason through all of that. Over time, there’s this larger process of whatever analogy you want to use—maybe RL, something else—where culture as a whole has figured out how to find and kill and eat seals.
In his view, what is happening when this knowledge is transmitted through generations, is that you have to imitate your elders in order to learn that skill. You can’t think your way through how to hunt and kill and process a seal. You have to watch other people, maybe make tweaks and adjustments, and that’s how knowledge accumulates. The initial step of the cultural gain has to be imitation. But maybe you think about it a different way?
Richard Sutton 00:21:00
No, I think about it the same way. Still, it’s a small thing on top of basic trial-and-error learning, prediction learning. It’s what distinguishes us, perhaps, from many animals. But we’re an animal first. We were an animal before we had language and all those other things.
Dwarkesh Patel 00:21:25
I do think you make a very interesting point that continual learning is a capability that most mammals have. I guess all mammals have it. It’s quite interesting that we have something that all mammals have, but our AI systems don’t have. Whereas the ability to understand math and solve difficult math problems—depends on how you define math—is a capability that our AIs have, but that almost no animal has. It’s quite interesting what ends up being difficult and what ends up being easy.
Richard Sutton 00:21:57
Moravec’s paradox.
Dwarkesh Patel 00:21:58
That’s right, that’s right.
00:23:10 – The Era of Experience
Dwarkesh Patel 00:23:10
This alternative paradigm that you’re imagining…
Richard Sutton 00:23:12
The experiential paradigm. Let’s lay it out a little bit. It says that experience, action, sensation—well, sensation, action, reward—this happens on and on and on for your life. It says that this is the foundation and the focus of intelligence. Intelligence is about taking that stream and altering the actions to increase the rewards in the stream.
Learning then is from the stream, and learning is about the stream. That second part is particularly telling. What you learn, your knowledge, is about the stream. Your knowledge is about if you do some action, what will happen. Or it’s about which events will follow other events. It’s about the stream. The content of the knowledge is statements about the stream. Because it’s a statement about the stream, you can test it by comparing it to the stream, and you can learn it continually.
Dwarkesh Patel 00:24:19
When you’re imagining this future continual learning agent…
Richard Sutton 00:24:22
They’re not “future”. Of course, they exist all the time. This is what the reinforcement learning paradigm is, learning from experience.
Dwarkesh Patel 00:24:29
Yeah, I guess what I meant to say is a general human-level, general continual learning agent. What is the reward function? Is it just predicting the world? Is it then having a specific effect on it? What would the general reward function be?
Richard Sutton 00:24:46
The reward function is arbitrary. If you’re playing chess, it’s to win the game of chess. If you’re a squirrel, maybe the reward has to do with getting nuts. In general, for an animal, you would say the reward is to avoid pain and to acquire pleasure. I think there also should be a component having to do with your increasing understanding of your environment. That would be sort of an intrinsic motivation.
Dwarkesh Patel 00:25:27
I see. With this AI, lots of people would want it to be doing lots of different kinds of things. It’s performing the task people want, but at the same time, it’s learning about the world from doing that task.
Let’s say we get rid of this paradigm where there’s training periods and then there’s deployment periods. Do we also get rid of this paradigm where there’s the model and then instances of the model or copies of the model that are doing certain things? How do you think about the fact that we’d want this thing to be doing different things? We’d want to aggregate the knowledge that it’s gaining from doing those different things.
Richard Sutton 00:26:11
I don’t like the word “model” when used the way you just did. I think a better word would be “the network” because I think you mean the network. Maybe there are many networks. Anyway, things would be learned. You’d have copies and many instances. Sure, you’d want to share knowledge across the instances. There would be lots of possibilities for doing that.
Today, you have one child grow up and learn about the world, and then every new child has to repeat that process. Whereas with AIs, with a digital intelligence, you could hope to do it once and then copy it into the next one as a starting place. This would be a huge savings. I think it’d be much more important than trying to learn from people.
Dwarkesh Patel 00:27:02
I agree that the kind of thing you’re talking about is necessary regardless of whether you start from LLMs or not. If you want human or animal-level intelligence, you’re going to need this capability.
Suppose a human is trying to make a startup. This is a thing which has a reward on the order of 10 years. Once in 10 years you might have an exit where you get paid out a billion dollars. But humans have this ability to make intermediate auxiliary rewards or have some way of…Even when they have extremely sparse rewards, they can still make intermediate steps having an understanding of what the next thing they’re doing leads to this grander goal we have. How do you imagine such a process might play out with AIs?
Richard Sutton 00:27:43
This is something we know very well. The basis of it is temporal difference learning where the same thing happens in a less grandiose scale. When you learn to play chess, you have the long-term goal of winning the game. Yet you want to be able to learn from shorter-term things like taking your opponent’s pieces.
You do that by having a value function which predicts the long-term outcome. Then if you take the guy’s pieces, your prediction about the long-term outcome is changed. It goes up, you think you’re going to win. Then that increase in your belief immediately reinforces the move that led to taking the piece.
We have this long-term 10-year goal of making a startup and making a lot of money. When we make progress, we say, “Oh, I’m more likely to achieve the long-term goal,” and that rewards the steps along the way.
Dwarkesh Patel 00:28:47
You also want some ability for information that you’re learning. One of the things that makes humans quite different from these LLMs is that if you’re onboarding on a job, you’re picking up so much context and information. That’s what makes you useful at the job. You’re learning everything from how your client has preferences to how the company works, everything.
Is the bandwidth of information that you get from a procedure like TD learning high enough to have this huge pipe of context and tacit knowledge that you need to be picking up in the way humans do when they’re just deployed?
Richard Sutton 00:29:27
I’m not sure but I think at the crux of this, the big world hypothesis seems very relevant. The reason why humans become useful on the job is because they are encountering their particular part of the world. It can’t have been anticipated and can’t all have been put in in advance. The world is so huge that you can’t.
The dream of large language models, as I see it, is you can teach the agent everything. It will know everything and won’t have to learn anything online, during its life. Your examples are all, “Well, really you have to” because you can teach it, but there’s all the little idiosyncrasies of the particular life they’re leading and the particular people they’re working with and what they like, as opposed to what average people like. That’s just saying the world is really big, and you’re going to have to learn it along the way.
Dwarkesh Patel 00:30:28
It seems to me you need two things. One is some way of converting this long-run goal reward into smaller auxiliary predictive rewards of the future reward, or the future reward that leads to the final reward. But initially, it seems to me, I need to hold on to all this context that I’m gaining as I’m working in the world. I’m learning about my clients, my company, and all this information.
Richard Sutton 00:31:04
I would say you’re just doing regular learning. Maybe you’re using “context” because in large language models all that information has to go into the context window. But in a continual learning setup, it just goes into the weights.
Dwarkesh Patel 00:31:17
Maybe context is the wrong word to use because I mean a more general thing.
Richard Sutton 00:31:20
You learn a policy that’s specific to the environment that you’re finding yourself in.
Dwarkesh Patel 00:31:25
The question I’m trying to ask is, you need some way of getting…How many bits per second is a human picking up when they’re out in the world? If you’re just interacting over Slack with your clients and everything.
Richard Sutton 00:31:41
Maybe you’re trying to ask the question of, it seems like the reward is too small of a thing to do all the learning that we need to do. But we have the sensations, we have all the other information we can learn from. We don’t just learn from the reward. We learn from all the data.
Dwarkesh Patel 00:31:59
What is the learning process which helps you capture that information?
Richard Sutton 00:32:06
Now I want to talk about the base common model of the agent with the four parts.
We need a policy. The policy says, “In the situation I’m in, what should I do?” We need a value function. The value function is the thing that is learned with TD learning, and the value function produces a number. The number says how well it’s going. Then you watch if that’s going up and down and use that to adjust your policy. So you have those two things. Then there’s also the perception component, which is construction of your state representation, your sense of where you are now.
The fourth one is what we’re really getting at, most transparently anyway. The fourth one is the transition model of the world. That’s why I am uncomfortable just calling everything “models,” because I want to talk about the model of the world, the transition model of the world. Your belief that if you do this, what will happen? What will be the consequences of what you do? Your physics of the world. But it’s not just physics, it’s also abstract models, like your model of how you traveled from California up to Edmonton for this podcast. That was a model, and that’s a transition model. That would be learned. It’s not learned from reward. It’s learned from, “You did things, you saw what happened, you made that model of the world.”
That will be learned very richly from all the sensation that you receive, not just from the reward. It has to include the reward as well, but that’s a small part of the whole model, a small, crucial part of the whole model.
00:33:39 – Current architectures generalize poorly out of distribution
Dwarkesh Patel 00:33:39
One of my friends, Toby Ord, pointed out that if you look at the MuZero models that Google DeepMind deployed to learn Atari games, these models were initially not a general intelligence itself, but a general framework for training specialized intelligences to play specific games. That is to say that you couldn’t, using that framework, train a policy to play both chess and Go and some other game. You had to train each one in a specialized way.
He was wondering whether that implies that with reinforcement learning generally, because of this information constraint, you can only learn one thing at a time? The density of information isn’t that high? Or whether it was just specific to the way that MuZero was done. If it’s specific to AlphaZero, what needed to be changed about that approach so that it could be a general learning agent?
Richard Sutton 00:34:35
The idea is totally general. I do use all the time, as my canonical example, the idea of an AI agent is like a person. People, in some sense, have just one world they live in. That world may involve chess and it may involve Atari games, but those are not a different task or a different world. Those are different states they encounter. So the general idea is not limited at all.
Dwarkesh Patel 00:35:06
Maybe it would be useful to explain what was missing in that architecture, or that approach, which this continual learning AGI would have.
Richard Sutton 00:35:19
They just set it up. It was not their ambition to have one agent across those games. If we want to talk about transfer, we should talk about transfer not across games or across tasks, but transfer between states.
Dwarkesh Patel 00:35:36
I guess I’m curious if historically, have we seen the level of transfer using RL techniques that would be needed to build this kind of…
Richard Sutton 00:35:49
Good. Good. We’re not seeing transfer anywhere. Critical to good performance is that you can generalize well from one state to another state. We don’t have any methods that are good at that. What we have are people trying different things and they settle on something, a representation that transfers well or generalizes well. But we have very few automated techniques to promote transfer, and none of them are used in modern deep learning.
Dwarkesh Patel 00:36:26
Let me paraphrase to make sure that I understood that correctly. It sounds like you’re saying that when we do have generalization in these models, that is a result of some sculpted…
Richard Sutton 00:36:42
Humans did it. The researchers did it. Because there’s no other explanation. Gradient descent will not make you generalize well. It will make you solve the problem. It will not make you, if you get new data, generalize in a good way.
Generalization means to train on one thing that’ll affect what you do on other things. We know deep learning is really bad at this. For example, we know that if you train on some new thing, it will often catastrophically interfere with all the old things that you knew. This is exactly bad generalization.
Generalization, as I said, is some kind of influence of training on one state on other states. The fact that you generalize is not necessarily good or bad. You can generalize poorly, you can generalize well. Generalization always will happen, but we need algorithms that will cause the generalization to be good rather than bad.
Dwarkesh Patel 00:37:41
I’m not trying to kickstart this initial crux again, but I’m just genuinely curious because I think I might be using the term differently. One way to think about these LLMs is that they’re increasing the scope of generalization from earlier systems, which could not really even do a basic math problem, to now where they can do anything in this class of Math Olympiad-type problems.
You initially start with them being able to generalize among addition problems. Then they can generalize among problems which require use of different kinds of mathematical techniques and theorems and conceptual categories, which is what the Math Olympiad requires. It sounds like you don’t think of being able to solve any problem within that category as an example of generalization. Let me know if I’m misunderstanding that.
Richard Sutton 00:38:33
Large language models are so complex. We don’t really know what information they have had prior. We have to guess because they’ve been fed so much. This is one reason why they’re not a good way to do science. It’s just so uncontrolled, so unknown.
Dwarkesh Patel 00:38:52
But if you come up with an entirely new…
Richard Sutton 00:38:54
They’re getting a bunch of things right, perhaps. The question is why. Well maybe that they don’t need to generalize to get them right, because the only way to get some of them right is to form something which gets all of them right. If there’s only one answer and you find it, that’s not called generalization. It’s just it’s the only way to solve it, and so they find the only way to solve it. But generalization is when it could be this way, it could be that way, and they do it the good way.
Dwarkesh Patel 00:39:24
My understanding is that this is working better and better, with coding agents. With engineers, obviously if you’re trying to program a library, there are many different ways you could achieve the end spec. An initial frustration with these models has been that they’ll do it in a way that’s sloppy. Over time they’re getting better and better at coming up with the design architecture and the abstractions that developers find more satisfying. It seems like an example of what you’re talking about.
Richard Sutton 00:39:56
There’s nothing in them which will cause it to generalize well. Gradient descent will cause them to find a solution to the problems they’ve seen. If there’s only one way to solve them, they’ll do that. But if there are many ways to solve it, some which generalize well, some which generalize poorly, there’s nothing in the algorithms that will cause them to generalize well. But people, of course, are evolved and if it’s not working out they fiddle with it until they find a way, perhaps until they find a way which generalizes well.
00:41:29 – Surprises in the AI field
Dwarkesh Patel 00:41:29
I want to zoom out and ask about being in the field of AI for longer than almost anybody who is commentating on it, or working in it now. I’m curious about what the biggest surprises have been. How much new stuff do you feel like is coming out? Or does it feel like people are just playing with old ideas? Zooming out, you got into this even before deep learning was popular. So how do you see the trajectory of this field over time and how new ideas have come about and everything? What’s been surprising?
Richard Sutton 00:42:06
I thought a little bit about this. There are a handful of things. First, the large language models are surprising. It’s surprising how effective artificial neural networks are at language tasks. That was a surprise, it wasn’t expected. Language seemed different. So that’s impressive.
There’s a long-standing controversy in AI about simple basic principle methods, the general-purpose methods like search and learning, compared to human-enabled systems like symbolic methods. In the old days, it was interesting because things like search and learning were called weak methods because they’re just using general principles, they’re not using the power that comes from imbuing a system with human knowledge. Those were called strong. I think the weak methods have just totally won. That’s the biggest question from the old days of AI, what would happen. Learning and search have just won the day.
There’s a sense in which that was not surprising to me because I was always hoping or rooting for the simple basic principles. Even with the large language models, it’s surprising how well it worked, but it was all good and gratifying. AlphaGo was surprising, how well that was able to work, AlphaZero in particular. But it’s all very gratifying because again, simple basic principles are winning the day.
Dwarkesh Patel 00:44:00
Whenever the public conception has been changed because some new application was developed— for example, when AlphaZero became this viral sensation—to you as somebody who has literally came up with many of the techniques that were used, did it feel to you like new breakthroughs were made? Or did it feel like, “Oh, we’ve had these techniques since the ‘90s and people are simply combining them and applying them now”?
Richard Sutton 00:44:28
The whole AlphaGo thing had a precursor, which is TD-Gammon. Gerry Tesauro did reinforcement learning, temporal difference learning methods, to play backgammon. It beat the world’s best players and it worked really well. In some sense, AlphaGo was merely a scaling up of that process. But it was quite a bit of scaling up and there was also an additional innovation in how the search was done. But it made sense. It wasn’t surprising in that sense.
AlphaGo actually didn’t use TD learning. It waited to see the final outcomes. But AlphaZero used TD. AlphaZero was applied to all the other games and it did extremely well. I’ve always been very impressed by the way AlphaZero plays chess because I’m a chess player and it just sacrifices material for positional advantages. It’s just content and patient to sacrifice that material for a long period of time. That was surprising that it worked so well, but also gratifying and it fit into my worldview.
This has led me where I am. I’m in some sense a contrarian or someone thinking differently than the field is. I’m personally just content being out of sync with my field for a long period of time, perhaps decades, because occasionally I have been proved right in the past. The other thing I do—to help me not feel I’m out of sync and thinking in a strange way—is to look not at my local environment or my local field, but to look back in time and into history and to see what people have thought classically about the mind in many different fields. I don’t feel I’m out of sync with the larger traditions. I really view myself as a classicist rather than as a contrarian. I go to what the larger community of thinkers about the mind have always thought.
00:46:41 – Will The Bitter Lesson still apply after AGI?
Dwarkesh Patel 00:46:41
Some sort of left-field questions for you if you’ll tolerate them. The way I read the bitter lesson is that it’s not necessarily saying that human artisanal researcher tuning doesn’t work, but that it obviously scales much worse than compute, which is growing exponentially. So you want techniques which leverage the latter.
Richard Sutton 00:47:06
Yep.
Dwarkesh Patel 00:47:07
Once we have AGI, we’ll have researchers which scale linearly with compute. We’ll have this avalanche of millions of AI researchers. Their stock will be growing as fast as compute. So maybe this will mean that it is rational or it will make sense to have them doing good old-fashioned AI and doing these artisanal solutions. As a vision of what happens after AGI in terms of how AI research will evolve, I wonder if that’s still compatible with a bitter lesson.
Richard Sutton 00:47:40
How did we get to this AGI? You want to presume that it’s been done.
Dwarkesh Patel 00:47:45
Suppose it started with general methods, but now we’ve got the AGI. And now we want to go…
Richard Sutton 00:47:52
Then we’re done.
Dwarkesh Patel 00:47:53
Interesting. You don’t think that there’s anything above AGI?
Richard Sutton 00:47:58
But you’re using it to get AGI again.
Dwarkesh Patel 00:48:01
Well, I’m using it to get superhuman levels of intelligence or competence at different tasks.
Richard Sutton 00:48:05
These AGIs, if they’re not superhuman already, then the knowledge that they might impart would be not superhuman.
Dwarkesh Patel 00:48:15
I guess there are different gradations.
Richard Sutton 00:48:16
I’m not sure your idea makes sense because it seems to presume the existence of AGI and that we’ve already worked that out.
Dwarkesh Patel 00:48:27
Maybe one way to motivate this is, AlphaGo was superhuman. It beat any Go player. AlphaZero would beat AlphaGo every single time. So there are ways to get more superhuman than even superhuman. It was also a different architecture. So it seems possible to me that the agent that’s able to generally learn across all domains, there would be ways to give it better architecture for learning, just the same way that AlphaZero was an improvement upon AlphaGo and MuZero was an improvement upon AlphaZero.
Richard Sutton 00:48:56
And the way AlphaZero was an improvement was that it did not use human knowledge but just went from experience.
Dwarkesh Patel 00:49:04
Right.
Richard Sutton 00:49:04
So why do you say, “Bring in other agents’ expertise to teach it”, when it’s worked so well from experience and not by help from another agent?
Dwarkesh Patel 00:49:19
I agree that in that particular case that it was moving to more general methods. I meant to use that particular example to illustrate that it’s possible to go superhuman to superhuman++, to superhuman+++. I’m curious if you think those gradations will continue to happen by just making the method simpler. Or, because we’ll have the capability of these millions of minds who can then add complexity as needed, will that continue to be a false path, even when you have billions of AI researchers or trillions of AI researchers?
Richard Sutton 00:49:51
It’s more interesting just to think about that case. When you have many AIs, will they help each other the way cultural evolution works in people? Maybe we should talk about that. The bitter lesson, who cares about that? That’s an empirical observation about a particular period in history. 70 years in history, it doesn’t necessarily have to apply to the next 70 years.
An interesting question is, you’re an AI, you get some more computer power. Should you use it to make yourself more computationally capable? Or should you use it to spawn off a copy of yourself to go learn something interesting on the other side of the planet or on some other topic and then report back to you?
I think that’s a really interesting question that will only arise in the age of digital intelligences. I’m not sure what the answer is. More questions, will it be possible to really spawn it off, send it out, learn something new, something perhaps very new, and then will it be able to be reincorporated into the original? Or will it have changed so much that it can’t really be done? Is that possible or is that not? You could carry this to its limit as I saw one of your videos the other night. It suggests that it could. You spawn off many, many copies, do different things, highly decentralized, but report back to the central master. This will be such a powerful thing.
This is my attempt to add something to this view. A big issue will become corruption. If you really could just get information from anywhere and bring it into your central mind, you could become more and more powerful. It’s all digital and they all speak some internal digital language. Maybe it’ll be easy and possible. But it will not be as easy as you’re imagining because you can lose your mind this way. If you pull in something from the outside and build it into your inner thinking, it could take over you, it could change you, it could be your destruction rather than your increment in knowledge.
I think this will become a big concern, particularly when you’re like, “Oh, he’s figured out all about how to play some new game or he’s studied Indonesia, and you want to incorporate that into your mind.” You could think, “Oh, just read it all in, and that’ll be fine.” But no, you’ve just read a whole bunch of bits into your mind, and they could have viruses in them, they could have hidden goals, they can warp you and change you. This will become a big thing. How do you have cybersecurity in the age of digital spawning and re-reforming again?
00:53:48 – Succession to AI
Dwarkesh Patel 00:53:48
I guess this brings us to the topic of AI succession. You have a perspective that’s quite different from a lot of people that I’ve interviewed and a lot of people generally. I also think it’s a very interesting perspective. I want to hear about it.
Richard Sutton 00:54:03
I do think succession to digital intelligence or augmented humans is inevitable. I have a four-part argument. Step one is, there’s no government or organization that gives humanity a unified point of view that dominates and that can arrange... There’s no consensus about how the world should be run. Number two, we will figure out how intelligence works. The researchers will figure it out eventually. Number three, we won’t stop just with human-level intelligence. We will reach superintelligence. Number four, it’s inevitable over time that the most intelligent things around would gain resources and power.
Put all that together and it’s sort of inevitable. You’re going to have succession to AI or to AI-enabled, augmented humans. Those four things seem clear and sure to happen. But within that set of possibilities, there could be good outcomes, less good outcomes, and bad outcomes. I’m just trying to be realistic about where we are and ask how we should feel about it.
Dwarkesh Patel 00:55:35
I agree with all four of those arguments and the implication. I also agree that succession contains a wide variety of possible futures. Curious to get more thoughts on that.
Richard Sutton 00:55:50
I do encourage people to think positively about it. First of all, it’s something we humans have always tried to do for thousands of years, try to understand ourselves, trying to make ourselves think better, just understanding ourselves. This is a great success for science, humanities. We’re finding out what this essential part of humanness is, what it means to be intelligent.
Then what I usually say is that this is all human-centric. But if we step aside from being a human and just take the point of view of the universe, this is I think a major stage in the universe, a major transition, a transition from replicators. We humans and animals, plants, we’re all replicators. That gives us some strengths and some limitations.
We’re entering the age of design because our AIs are designed. Our physical objects are designed, our buildings are designed, our technology is designed. We’re designing AIs now, things that can be intelligent themselves and that are themselves capable of design. This is a key step in the world and in the universe. It’s the transition from the world in which most of the interesting things that are, are replicated. Replicated means you can make copies of them, but you don’t really understand them. Right now we can make more intelligent beings, more children, but we don’t really understand how intelligence works.
Whereas we’re reaching now to having designed intelligence, intelligence that we do understand how it works. Therefore we can change it in different ways and at different speeds than otherwise. In our future, they may not be replicated at all. We may just design AIs, and those AIs will design other AIs, and everything will be done by design and construction rather than by replication.
I mark this as one of the four great stages of the universe. First there’s dust, it ends with stars. Stars make planets. The planets can give rise to life. Now we’re giving rise to designed entities. I think we should be proud that we are giving rise to this great transition in the universe.
It’s an interesting thing. Should we consider them part of humanity or different from humanity? It’s our choice. It’s our choice whether we should say, “Oh, they are our offspring and we should be proud of them and we should celebrate their achievements.”Or we could say, “Oh no, they’re not us and we should be horrified.” It’s interesting that it feels to me like a choice. Yet it’s such a strongly held thing that, how could it be a choice? I like these sort of contradictory implications of thought.
Dwarkesh Patel 00:58:57
It is interesting to consider if we are just designing another generation of humans. Maybe design is the wrong word. But we know a future generation of humans is going to come up. Forget about AI. We just know in the long run, humanity will be more capable and more numerous, maybe more intelligent. How do we feel about that? I do think there are potential worlds with future humans that we would be quite concerned about.
Richard Sutton 00:59:22
Are you thinking like, maybe we are like the Neanderthals that give rise to Homo sapiens. Maybe Homo sapiens will give rise to a new group of people.
Dwarkesh Patel 00:59:32
Something like that. I’m basically taking the example you’re giving. Even if we consider them part of humanity, I don’t think that necessarily means that we should feel super comfortable.
Richard Sutton 00:59:42
Kinship.
Dwarkesh Patel 00:59:43
Like Nazis were humans, right? If we thought, “Oh, the future generation will be Nazis, I think we’d be quite concerned about just handing off power to them.” So I agree that this is not super dissimilar to worrying about more capable future humans, but I don’t think that addresses a lot of the concerns people might have about this level of power being attained this fast with entities we don’t fully understand.
Richard Sutton 01:00:09
I think it’s relevant to point out that for most of humanity, they don’t have much influence on what happens. Most of humanity doesn’t influence who can control the atom bombs or who controls the nation states. Even as a citizen, I often feel that we don’t control the nation states very much. They’re out of control.
A lot of it has to do with just how you feel about change. If you think the current situation is really good, then you’re more likely to be suspicious of change and averse to change than if you think it’s imperfect. I think it’s imperfect. In fact, I think it’s pretty bad. So I’m open to change. I think humanity has not had a super good track record. Maybe it’s the best thing that there has been, but it’s far from perfect.
Dwarkesh Patel 01:01:13
I guess there are different varieties of change. The Industrial Revolution was change, the Bolshevik Revolution was also change. If you were around in Russia in the 1900s and you were like, “Look, things aren’t going well, the tsar is kind of messing things up, we need change”, I’d want to know what kind of change you wanted before signing on the dotted line. Similarly with AI, where I’d want to understand and, to the extent that it’s possible, change the trajectory of AI such that the change is positive for humans.
Richard Sutton 01:01:49
We should be concerned about our future, the future. We should try to make it good. We should also though recognize the limit, our limits. I think we want to avoid the feeling of entitlement, avoid the feeling of, “Oh, we are here first, we should always have it in a good way.” How should we think about the future? How much control should a particular species on a particular planet have over it? How much control do we have? A counterbalance to our limited control over the long-term future of humanity should be how much control do we have over our own lives. We have our own goals. We have our families. Those things are much more controllable than trying to control the whole universe.
I think it’s appropriate for us to really work towards our own local goals. It’s kind of aggressive for us to say, “Oh, the future has to evolve this way that I want it to.” Because then we’ll have arguments where different people think the global future should evolve in different ways, and then they have conflict. We want to avoid that.
Dwarkesh Patel 01:03:13
Maybe a good analogy here would be this. Suppose you are raising your own children. It might not be appropriate to have extremely tight goals for their own life, or also have some sense of like, “I want my children to go out there in the world and have this specific impact. My son’s going to become president and my daughter is going to become CEO of Intel. Together they’re going to have this effect on the world.”
But people do have the sense—and I think this is appropriate—of saying, “I’m going to give them good robust values such that if and when they do end up in positions of power, they do reasonable, prosocial things.” Maybe a similar attitude towards AI makes sense, not in the sense of we can predict everything that they will do, or we have this plan about what the world should look like in a hundred years. But it’s quite important to give them robust and steerable and prosocial values.
Richard Sutton 01:04:13
Prosocial values?
Dwarkesh Patel 01:04:15
Maybe that’s the wrong word.
Richard Sutton 01:04:16
Are there universal values that we can all agree on?
Dwarkesh Patel 01:04:21
I don’t think so, but that doesn’t prevent us from giving our kids a good education, right? Like we have some sense of wanting our children to be a certain way.
Maybe prosocial is the wrong word. High integrity is maybe a better word. If there’s a request or if there’s a goal that seems harmful, they will refuse to engage in it. Or they’ll be honest, things like that. We have some sense that we can teach our children things like this, even if we don’t have some sense of what true morality is, where everybody doesn’t agree on that. Maybe that’s a reasonable target for AI as well.
Richard Sutton 01:04:57
So we’re trying to design the future and the principles by which it will evolve and come into being. The first thing you’re saying is, “Well, we try to teach our children general principles which will promote more likely evolutions.” Maybe we should also seek for things to be voluntary. If there is change, we want it to be voluntary rather than imposed on people. I think that’s a very important point. That’s all good.
I think this is the big or one of the really big human enterprises to design society that’s been ongoing for thousands of years again. The more things change, the more things they stay the same. We still have to figure out how to be. The children will still come up with different values that seem strange to their parents and their grandparents. Things will evolve.
Dwarkesh Patel 01:05:57
“The more things change, the more they stay the same” also seems like a good capsule into the AI discussion. The AI discussion we were having was about how techniques, which were invented even before their application to deep learning and backpropagation was evident, are central to the progression of AI today. Maybe that’s a good place to wrap up the conversation.
Richard Sutton 01:06:19
Okay. Thank you very much.
Dwarkesh Patel 01:06:21
Awesome. Thank you for coming on.
Richard Sutton 01:06:22
My pleasure.
