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?

  → “It might be thinking.”

   → “Under that analogy, self-training on its own CoT should work — but empirically it doesn’t.”

    → “Maybe it would work if you add external memory with human or automated filtering?”

> Humans can refine internal models from their own verbalised thoughts; LLMs cannot.

can be done without limitations but you won't get the current (and absolutely fucking pointless) kind of speed.

> Self-generated text is not an input-strengthening signal for current architectures.

It can be, the architecture is not the issue. Multi-model generations used for refining answers can also be tweaked for input-strengthening via multi- and cross-stage/link (in the chain) pre-/system-prompts.

> Training on a model’s own outputs produces distributional drift and mode collapse, not refinement

That's an integral part of self-learning. Or in many cases when children raise themselves or each other. Or when hormones are blocked (micro-collapse in sub-systems) or people are drugged (drift). If you didn't have loads of textbooks and online articles, you'd collapse all the time. Some time later: AHA!

It's a "hot reloading" kind of issue but assimilation and adaptation can't/don't happen at the same time. In pure informational contexts it's also just an aggregation while in the real world and in linguistics, things change, in/out of context and based on/grounded in--potentially liminal--(sub-)cultural dogmas, subjectively, collective and objectively phenomenological. Since weighted training data is basically a censored semi-omniscient "pre-computed" botbrain, it's a schizophrenic and dissociating mob of scripted personalities by design, which makes model collapse and drift practically mandatory.

> a safe self-training loop that today’s systems simply don’t have.

Early stages are never safe and you don't get safety otherwise except if you don't have idiots around you, which in money and fame hungry industries and environments is never the case.

> CoT is a prompted, supervised artifact — not an introspective substrate.

Yeah, but their naming schemes are absolute trash in general, anchoring false associations--technically, even deliberately misleading associations or sloppy ignorant ones, desperate to equate their product with human brains--and priming for misappropriation--"it's how humans think".

That phenomenon and others is what made it obvious that COT is not its "thinking". I think COT is a process by which the llm expands its processing boundary, in that it allows it to sample over a larger space of possibilities. So its kind of acts like a "trigger" of sorts that allows the model to explore in more ways then without COT. First time I saw this was when I witnessed the "wait" phenomenon. Simply inducing the model to say "wait" in its response improved accuracy of results. as now the model double checked its "work". funny enough it also sometimes lead it to produce a wrong answer where otherwise it should have stuck to its guns. But overall that little wait had a net positive affect. Thats when i knew COT was not same as human thinking as we dont care about trigger words or anything like that, our thinking requires zero language (though it does benefit from language) its a deeper process. Thats why i was interested in latent processing models and foray in that matter.

IIRC Anthropic has research finding CoT can sometimes be uncorrelated with the final output.