I think the path of least resistance will end up being the cheapest and that is scaling up the parameters a ridiculous amount until you get highly capable models that can develop/distill/design the RAM efficient models. Going straight for low param is foolish and just a cope by smaller labs because they don't have the compute/talent to train the large ones.

This is 100% true for pretrains, likely true for RL as well although maybe there is some benefit to smaller activated params there. There is of course 0 benefit to small dense models relative to large sparse ones that are equally as memory efficient if you have enough computers.

Many on HN are in deep denial about this imo.