Taking actions that mutate the environment is a different story. I think this is where you run into diminishing returns very quickly. You generally want one strong agent to act given the results of all the searching that was done. If the plan is clear, you don't need a genius model to execute it.
https://arxiv.org/abs/2512.24601
There's also a great write up here by the author:
RLM might be more useful on the execution side than on the research side. In fact, these somehow feel like they might be exact inverses of each other in terms of what the ideal architecture looks like. At some point you definitely do need something in the middle that has it all sorted out.
Research tasks benefit the most from this. Because such work benefits from having a large number of agents working on a problem in parallel (i.e. crawling the web), and the model size becomes less important past a certain minimum.
I don't know about other categories of work, like programming. I imagine looking for bugs or security issues would benefit from it.