The entire second on chess engines is, from the view of the entire thesis of TFA, is incoherent. Let's assume, for sake of argument, that I agree with the section: that an idealized chess move predictor isn't a predictor — which is not a thing that exists, as the space of chess is enormous, but let's pretend! — that's not what LLMs are? Even if we just restrict ourselves to the space of written English prose, the space is quite literally infinite. So, hopefully obviously, no LLM is comparable to an idealized chess engine. Similarly, incoherently, we wave away the "make_more_likely", when, at least to me, the entire meat of that argument would be in the reward function, and we just gloss over that entirely.

(I would also agree with the parent commenter on that the writing smells like an LLM.)

I'm not sure what you want me to do with that information; clearly I do think that my description is accurate.

The article is littered with both AI tells and admissions that 'next token prediction' is what is happening. Hence my description.