So why then do we stop training LLMs and keep them stored at a specific state? Is it perhaps because the results become terrible and LLMs have a delicate optimal state for general use? This sounds like an even worse case for a model of intelligence.
Not entirely a straw man. What is the purpose of storing and retrieving LLMs at a fixed state if not to guarantee a specific performance? Wouldn’t a strong model of intelligence be capable of, to extend your analogy, running without having its hippocampus lobotomized?
Given the precariousness of managing LLM context windows, I don’t think it’s particularly unfair to assume that LLMs that learn without limit become very unstable.
To steelman, if it’s possible, it may be prohibitively expensive. But somehow I doubt it’s possible.
