This is true for the pre-training step. What if advancements in the reinforcement learning steps performed later may benefit from more compute and more models p

This is true for the pre-training step. What if advancements in the reinforcement learning steps performed later may benefit from more compute and more models parameters? If right now the RL steps only help with sampling, that is, they only optimize the output of a given possible reply instead of the other (there are papers pointing at this: that if you generate many replies with just the common sampling methods, and you can verify correctness of the reply, then you discover that what RL helps with is selecting what was already potentially within the model output) this would be futile. But maybe advancements in the RL will do to LLMs what AlphaZero-alike models did with Chess/Go.