RL != only online learning.
There's a ton of research on offline and imitation-based RL where the training data isn't tied to an agents past policy - which is exactly what this article is pointing to.
From my understanding, RL is a tuning approach on LLMs, so the outcome is still the same kind of beast, albeit with a different parameter set.
So empirically, I actually thought that the lead companies would already be strongly focused on improving coding capabilities, since this is where LLMs are very effective, and where they have huge cashflows from token consumptions.
So, either the motivation isn't there, or they're already doing something like that, or they know it's not as effective as the approaches they already have.
I wonder which one it is.
What you're referring to is actually just one application of RL (RLHF). RL itself is much more than that