I've never directly worked with either, but isn't RL agent-competitions approaches (i.e. simulating games between agents with different parameter values and iterating on this agents) a form of genetic algorithms?

It's also worth noting that this is exactly the type of problem that genetic algorithms were made for: no gradients, highly multimodal.

"It is more memory-efficient than Adam as it only keeps track of the momentum."

While this is technically true, is this a joke?

Typically, large companies don't use a single model, but rather a large number of different models, all performing different tasks (recommending, filtering, etc). It would very difficult to describe the complete recommendations pipeline (i.e from user request to final candidate) in a single academic paper.

Lolz at dinging you for not doing 15 different things in one line. That's like getting penalized for including unit tests.