Yes it is. In RLHF and DPO you are optimizing the model output for human preferences. In the reinforcement fine tuning that was announced today you are optimizing the hidden chain of thought to arrive at a correct answer, as judged by a predefined grader.

I mean i think it could easily be PPO post training. if your point is that the rewards are different, sure