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. (E.g. 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”. 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 lea