I don't think that follows. We learn via our perception (i.e. our inputs from the outside world); our perception is mediated by some kind of filter, and memories get written or altered. An LLM on the other hand has static weights. You can provide an LLM a "facts.md" (or whatever) file that "teaches" it stuff that's not in its weights (via the context), and it can use the combination to produce something, but that set of information never modifies its core weights. You could imagine a robot with an LLM inside it that has all kinds of sensors, and processes that write the observations from those sensors into a bunch of databases which are RAG'ed into prompts to simulate learning and experiences, perhaps (jefe likes her coffee with almond milk). But my point is that, architecturally, they don't learn - and that's the bigger obstacle.
PS garcinias seem to be generally delicious.