When we think, our thoughts are composed of both nonverbal cognitive processes (we have access to their outputs, but generally lack introspective awareness of their inner workings), and verbalised thoughts (whether the “voice in your head” or actually spoken as “thinking out loud”).
Of course, there are no doubt significant differences between whatever LLMs are doing and whatever humans are doing when they “think” - but maybe they aren’t quite as dissimilar as many argue? In both cases, there is a mutual/circular relationship between a verbalised process and a nonverbal one (in the LLM case, the inner representations of the model)
The analogy breaks at the learning boundary.
Humans can refine internal models from their own verbalised thoughts; LLMs cannot.
Self-generated text is not an input-strengthening signal for current architectures.
Training on a model’s own outputs produces distributional drift and mode collapse, not refinement.
Equating CoT with “inner speech” implicitly assumes a safe self-training loop that today’s systems simply don’t have.
CoT is a prompted, supervised artifact — not an introspective substrate.
Models have some limited means of refinement available to themselves already: augment a model with any form of external memory, and it can learn by writing to its memory and then reading relevant parts of that accumulated knowledge back in the future. Of course, this is a lot more rigid than what biological brains can do, but it isn’t nothing.
Does “distributional drift and mode collapse” still happen if the outputs are filtered with respect to some external ground truth - e.g. human preferences, or even (in certain restricted domains such as coding) automated evaluations?