They probably had time to toss that example in the training soup.

Previous models from competitors usually got that correct, and the reasoning versions almost always did.

This kind of reflexive criticism isn't helpful, it's closer to a fully generalized counter-argument against LLM progress, whereas it's obvious to anyone that models today can do things they couldn't do six months ago, let alone 2 years back.

	▲	suddenlybananas 3 hours ago \| parent [-]
		I'm not denying any progress, I'm saying that reasoning failures that are simple which have gone viral are exactly the kind of thing that they will toss in the training data. Why wouldn't they? There's real reputational risks in not fixing it and no costs in fixing it.