I'm pretty much a layperson in this field, but I don't understand why we're trying to teach a stochastic text transformer to reason. Why would anyone expect that approach to work?
I would have thought the more obvious approach would be to couple it to some kind of symbolic logic engine. It might transform plain language statements into fragments conforming to a syntax which that engine could then parse deterministically. This is the Platonic ideal of reasoning that the author of the post pooh-poohs, I guess, but it seems to me to be the whole point of reasoning; reasoning is the application of logic in evaluating a proposition. The LLM might be trained to generate elements of the proposition, but it's too random to apply logic.
Problem is, even with symbolic logic, reasoning is not completely deterministic. Whether one can get to a set of given axioms from a given proposition is sometimes undecidable.
"return 1"