Really, how hard is it to follow HN guidelines and :
a) not imagine straw-man arguments and not imagine more (or less) than what was said
b) refrain from snarky and false ad hominems
None of what you said in no way conflicts with what I said, and again shows a fundamental misunderstanding.
Reasoning is (mostly) part of the post-training dataset. If you add a large majority of false (ie. paradoxical, irrational etc.) reasoning traces to those, you'll get a model that successfully replicates the false reasoning of humans. If you mix it in with true reasoning traces, I imagine you'll get infinite loop behaviour as the reasoning trace oscillates between the true and the false.
The original premise that truth is purely a function of the training dataset still stands... I'm not even sure what people are arguing here, as that seems quite trivially obvious?
Ah, sorry. I haven't recognized "all the high-level capabilities of an LLM come from the training data (presumably unlike humans, given the context of this thread)" in your wording. This is probably true. LLM structure probably has no inherent inductive bias that would amount to truth seeking. If you want to get a useless LLM, you can do it. OK, no disagreement here.
a) not imagine straw-man arguments and not imagine more (or less) than what was said
b) refrain from snarky and false ad hominems
None of what you said in no way conflicts with what I said, and again shows a fundamental misunderstanding.
Reasoning is (mostly) part of the post-training dataset. If you add a large majority of false (ie. paradoxical, irrational etc.) reasoning traces to those, you'll get a model that successfully replicates the false reasoning of humans. If you mix it in with true reasoning traces, I imagine you'll get infinite loop behaviour as the reasoning trace oscillates between the true and the false.
The original premise that truth is purely a function of the training dataset still stands... I'm not even sure what people are arguing here, as that seems quite trivially obvious?