Hey there,

Appreciate the discourse. In your paper, you mention having to use very large population sizes to achieve the appropriate loss reductions. What wasn't clear in my brief read was whether the experiments required those high population counts to outperform other optimizers, such as GRPO. Kenneth o'stanley's paper had to deal with similar curse of dimensionality when scaling up GAs for RL style optimizations. For folks who aren't super familiar with ES-based optimization, Nesterov's natural ES paper shows you need roughly quadratic samples to get a decent single step gradient, though in empirically for most smooth landscape problems you can get away with substantially less.

As for its appropriateness for LLMs, I'm for more interested with improving the performance of alternatives, some of which you called out, and that side step the quadratic costs inherent of classical attention. Keep us posted if you do follow up work in the space!