The LLM has one job, to make code that looks plausible. That's it. There's no logic gone into writing that bit of code. So the bugs often won't be like those a programmer makes. Instead, they can introduce a whole new class of bug that's way harder to debug.
https://news.ycombinator.com/item?id=44163194
https://news.ycombinator.com/item?id=44068943
Even if you do, it still optimizes tricking them. It will also optimize writing good programs, but you act like that's a well defined and measurable thing.
You can definitely still run into some of the problems eluded to in the first link. Think hacking unit tests, deception, etc -- but the bar is less "create a perfect RL environment" than "create an RL environment where solving the problem is easier than reward hacking." It might be possible to exploit a bug in the Lean 4 proof assistant to prove a mathematical statement, but I suspect it will usually be easier for an LLM to just write a correct proof. Current RL environments aren't as watertight as Lean 4, but there's certainly work to make them more watertight.
This is in no way a "solved" problem, but I do see it as a counter to your assertion that "This isn't a thing RL can fix." RL is powerful.
> Current paradigms are shifting towards RLVR, which absolutely can optimize good programs
But no, it's much more complex than you claim. RLVM can optimize for correct answers in the narrow domains where there are correct answers but it can't optimize good programs. There's a big difference.
You're right that Lean, Coq, and other ATPs can prove mathematical statements, but they also don't ensure that a program is good. There's frequently an infinite number of proofs that are correct, but most of those are terrible proofs.
This is the same problem all the coding benchmarks face. Even if the LLM isn't cheating, testing isn't enough. If it was we'd never do code review lol. I can pass a test with an algorithm that's O(n^3) despite there being an O(1) solution.
You're right that it makes it better, but it doesn't fix the underlying problem I'm discussing.
Not everything is verifiable.
Verifiability isn't enough.
If you'd like to prove me wrong in the former you're going to need to demonstrate that there are provably true statements to lots of things. I'm not expecting you to defy my namesake, nor will I ask you prove correctness and solve the related halting problem.
You can't prove an image is high fidelity. You can't prove a song sounds good. You can't prove a poem is a poem. You can't prove this sentence is English. The world is messy as fuck and most things are highly subjective.
But the problem isn't binary, it is continuous. I said we're using Justice Potter optimization, you can't even define what porn is. These definitions change over time, often rapidly!
You're forgetting about the tyrannical of metrics. Metrics are great, powerful tools that are incredibly useful. But if you think they're perfectly aligned with what you intend to measure then they become tools that work against you. Goodhart's Law. Metrics only work as guides. They're no different than any other powerful tool, if you use it wrong you get hurt.
If you really want to understand this I really encourage you to deep dive into this stuff. You need to get into the math. Into the weeds. You'll find a lot of help with metamathematics (i.e. my namesake), metaphysics (Ian Hacking is a good start), and such. It isn't enough to know the math, you need to know what the math means.