Your experience makes me think that the reason the models got a better success rate is not because they are better at reasoning, but rather because the problem made it to their training dataset.
Timeline is roughly:
Model developer notices a sometimes highly specific weak area -> ... -> RLHF'ers are asked to develop a bunch of very specific problems improving the weak area -> a few months go by -> A paper gets published that squeezes water out of stone to make AI headlines.
These researchers should just become RLHF'ers because their efforts aren't uncovering anything unknown and it's just being dressed up with a little statistics. And by the time the research is out, the the fixes are already identified internally, worked on, and nearing pushes.
I just realized AI research will be part of the AI bubble if it bursts. I don't think there was a .com research sub-bubble, so this might be novel.