I am not sure. Why do you need domain expertise beyond being able to draft a verifier for the problem? Once you have the verifier, it's just a matter of compute. You can argue that being a domain expert allows you to narrow down the search space for the LLM and save time/compute costs. This is partially true, but LLMs are getting better and better at search (they can already do end-to-end performance optimization faster than performance experts at a FAANG company I work at), and compute to maintain the same intelligence level is getting cheaper.

A concrete example: GPU performance optimization for a kernel. This was (and still is) a very niche domain with not many top-notch experts. But kernel performance and characteristics are easily verifiable. You can run the agent in a closed loop for it to improve iteratively (and people are already doing it, coming up with kernels better than human-written ones).

You see Tao's example because:

1. He is curious (so he asks detailed questions, which are not necessarily needed in a closed-loop optimization).

2. Verification in math is harder. Many math tasks used in RL are easily verifiable. But for advanced open conjectures that require long proofs, you cannot trust the proof directly from the LLM (so it's not as easily verifiable as basic math problems or code). The model needs to write it in Lean, and you still need to make sure the Lean implementation correctly captures the specification of the problem. So you still need a human for verification in advanced math. But I don't see why you would need this in domains like performance improvement.