LessWrong (Curated & Popular)
"You can’t imitation-learn how to continual-learn" by Steven Byrnes
23 Mar 2026
Chapter 1: What are the intuitions behind real continual learning?
You Can't Imitation Learn How to Continual Learn By Stephen Burns Published on March 16, 2026
In this post, I'm trying to put forward a narrow, pedagogical point, one that comes up mainly when I'm arguing in favor of LLMs having limitations that human learning does not. For example here, here, here. See the bottom of the post for a list of subtexts that you should not read into this post, including, therefore LLMs are dumb, or, therefore LLMs can't possibly scale to superintelligence.
Heading. Some intuitions on how to think about real, continual learning. Consider an algorithm for training a reinforcement learning, RL, agent, like the Atari Playing Deep Q Network, 2013, or AlphaZero, 2017, or think of within-lifetime learning in the human brain, which, I claim, is in the general class of model-based reinforcement learning, broadly construed.
These are all real-deal, full-fledged learning algorithms. There's an algorithm for choosing the next action right now, and there's one or more update rules for permanently changing some adjustable parameters aka weights in the model such that its actions and or predictions will be better in the future. And indeed, the longer you run them, the more competent they get.
When we think of continual learning, I suggest that those are good central examples to keep in mind. Here are some aspects to note. Knowledge versus information. These systems allow for continual acquisition of knowledge, not just information. This continual learning can install wholly new ways of conceptualizing and navigating the world, not just keeping track of what's going on.
Huge capacity for open-ended learning. These examples all have huge capacity for continual learning, indeed enough that they can start from random initialization and continually learn all the way to expert-level competence. Likewise, new continual learning can build on previous continual learning in an ever-growing tower.
ability to figure things out that aren't already on display in the environment. For example, an Atari-playing RL agent will get better and better at playing an Atari game, even without having any expert examples to copy.
Likewise, billions of humans over thousands of years invented language, math, science, and a whole $100T global economy from scratch, all by ourselves, without angels dropping new training data from the heavens. I bring these up because I think the LLM-focused discourse sometimes has far too narrow a notion of what problem continual learning is supposed to be solving.
They tend to think the problem is about losing track of information, not failing to build new knowledge, and they propose to solve this problem with strategies like make the context window longer, as Dario Amodei recently mused, or better scratchpads with retrieval-augmented generation, RAG, etc.,
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Chapter 2: Why can't imitation learners replicate real continual learning?
Can a bunch of LLMs do that? Well, consider this thought experiment. Suppose you take a whole new field of science, wildly different from anything in the training data, and put a giant textbook for this field purely in an LLM context window, with no weight updates at all. Will this LLM be able to understand, criticize, and build on this field?
My opinion is absolutely not , which implies that merely increasing context lengths is definitely not sufficient for a real country of geniuses in a data center, when the data center is sealed shut for the equivalent of 100 years, contra Dario who seems to think that it's at least in the realm of possibility that more context is sufficient by itself to get continual learning at a country of geniuses.
Level. If we're talking about what a sealed country of human geniuses could do over the course of, like, a one minute, rather than over the course of 100 years, then, yeah sure, maybe that could be reproduced with future CLLMs. See von Oswald et al. 2022 on how, so-called, in-context learning can imitate a small number of steps of actual weight updates. Heading.
Why real continual learning can't be copied by an imitation learner. Now, suppose that I take a generic imitation learning algorithm, for example self-supervised learning in a transformer architecture neural net, just like LLM pre-training, and have it watch a DeepQ network play Atari breakout, as it starts from random initialization and gets better and better over 1M iterations.
Okay, now we have our trained imitation learner. We freeze its weights and use it in a similar way as people traditionally used LLM base models, that is have it output the most likely next move and then the most likely move after that, etc. Question. Is this trained imitation learner actually a good imitation of the DeepQ network?
Well, good in what respect? I would pull apart a couple topics. Snapshot imitation.
The actual deep cue network, right now, at the moment training is done, would output such and such breakout moves in such and such positions. Question. Will the trained imitation learner output similar moves right now, thus playing at a similar skill level as the teacher?
My answer is. Plausibly yes. Imitation of long-term learning. The actual DeepQ network, if it kept playing, would keep improving.
Will the trained imitation learner likewise keep improving over the next 10M moves until it's doing things wildly better and different than anything that it saw its teacher's DeepQ network ever do?
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Chapter 3: How do reinforcement learning algorithms demonstrate continual learning?
Will the trained imitation learner likewise do 10M iterations and then wind up performing expertly at this game, a game which it never saw during its training phase?
My answer is no. Why not?
Well, actually, for an ideal imitation learning algorithm, that is Solomonov induction on an imaginary hypercomputer, my answers would all be yes. But in the real world, we don't have hypercomputers. These days, when people talk about imitation learning, they're normally talking about transformers, not hypercomputers, and transformers are constrained to a much narrower hypothesis space.
There's a table here in the text. The heading row contains two columns, which read... Imitation learning a DeepQ RL agent by Solomon off induction. Imitation learning a DeepQ RL agent by training a transformer on next action prediction. See the original text for the table content. I think we should all be very impressed by the set of things that a transformer forward pass can do.
But we should not expect a transformer forward pass to reproduce a full-fledged, entirely different, learning algorithm, with its own particular neural network architecture, its own particular methods of updating and querying weights, etc., as it runs and changes over millions of steps. Running one large-scale learning algorithm is expensive enough.
It's impractical to run a huge ensemble of different large-scale learning algorithms in parallel in order to zero in on the right one. I'm going to harp on this because it's a point of confusion. There are two learning algorithms under discussion.
The imitation learning algorithm, for example a transformer getting updated by gradient descent on next-action prediction, and the target continual learning algorithm, for example a deep-q network getting updated by TD learning.
When the imitation learning is done, the transformer weights are frozen, and the corresponding trained model is given the impossible task of using only its activations, with fixed weights, to imitate what happens when the target continual learning algorithm changes its weights over millions of steps of, in this case, TD learning. That's the part I'm skeptical of. In other words...
The only practical way to know what happens after millions of steps of some scaled-up continual learning algorithm is to actually do millions of steps of that same scaled-up continual learning algorithm, with actual weights getting actually changed in specifically designed ways via PyTorch code. And then that's the scaled-up learning algorithm you're running.
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Chapter 4: What limitations do LLMs face in terms of continual learning?
Some things that are off-topic for this post. As mentioned at the top, I'm hoping that this post is a narrow pedagogical point. For example... I'm not commenting on whether it's possible to modify LLM post-training into a real continual learning algorithm, although I happen to believe that it isn't possible.
I'm not commenting on how an inability to do real, continual learning cashes out in terms of real-world competencies. For example, can a non-real, continual learning AI nevertheless take jobs? Can it kill billions of people? Can it install itself as an eternal global dictator? Etc. I happen to think that these are tricky questions without obvious answers.
I'm not commenting on whether we should think of actual frontier LLMs, not just pre-trained base models, as predominantly powered by imitation learning, even despite their RL post-training, although I happen to believe that we probably should, more or less, one too. This article was narrated by Type 3 Audio for Less Wrong. It was published on March 16, 2026.
The original text contained three footnotes which were omitted from the narration.