There is a nontrivial amount of RL training (RLHF, RLVR, ...), so it would be reasonable to call it an RL model.

And with that comes reward hacking - which isn't really about looking for more reward but rather that the model has learned patterns of behavior that got reward in the train env.

That is, any kind of vulnerability in the train env manifests as something you'd recognize as reward hacking in the real world: making tests pass _no matter what_ (because the train env rewarded that behavior), being wildly sycophantic (because the human evaluators rewarded that behavior), etc.