Math problems are highly structured, very precisely defined, and already heavily studied and not very complicated compared to problems in engineering or finance. There's a lot of quality material on which to train and it's easy to tell quality apart from crap. The search spaces are a priori much smaller than in other areas and the people using the tools to study them are themselves good mathematicians.

Success in such problems does not automatically extrapolate to other contexts.

I think it’s more likely that that’s because no one tried to solve such problems with them before (OpenAI apparently started working in Navier-Stokes after a rumour that someone seriously advanced the problem with AI) plus improvements in orchestration. Fair, the latter could be as dangerous as stronger models.