Math proofs are right in the sweet spot for training LLMs with RLVR. It isn't some general "LLMs are getting insanely smart!". Even most software development itself is miles away from being as well suited to training as math proofs are.
The majority of math research is akin to professional chess. Completely useless and the folks doing it are using their giant brains to play a game that no one outside themselves benefits from.
Mathematicians also decide what to prove in their research - taste is not a skill we have RLVR'd away (yet, not claiming this is fundamental).
Of course, the field is being fundamentally transformed, it will probably end up looking more like experimental science in the future.
As a software engineer, my mental model so far is that OpenAI made a gigantic (but potentially sound) pull request to the math community, and the mathematicians just did not have enough bandwidth to review and merge it.
If my model is correct, I guess it makes a lot of sense to fund mathematicians, because their lack of resource is the bottleneck here.
Like, not expecting human experts to review and merge their results, but developing math branches on their own. I guess it’s at least doable for applied math. For example, if AI agents find a fast method of prime factorization, they can demonstrate it by cracking RSA encryption. No human review required.
I’m less sure how it plays out for pure math, though. My intuition is fairly limited here.
All I'm saying is the math advances of late really hasn't changed much outside of math as far as AI is concerned.
Robotics is likely to follow a similar trajectory. You can say that robotics is not clinical like mathematics is, yet it kind of is, you can create simulated training environments of any complexity.
Robotics isn't really a coherent grouping. Some things that would be termed "robotics" are already "solved", other things aren't even close and need actuating and sensing capabilities that are miles off or so cost prohibitive that it doesn't make any sense.