I would just want to comment that while this is true in principle, it's also slightly misleading because it does not include how much tuning and testing is necessary until one gets to this result.

Determining the scale needed, fiddling with the state/action/reward model, massively parallel hyper-parameter tuning.

I may be overestimating but I would reckon with hyper-parameter tuning and all that was easily in the 7-8 figure range for retail cost.

This is slightly frustrating in an academic environment when people tout results for just a few days of training (even with much smaller resources, say 16 gpus and 512 CPUs) when the cost of getting there is just not practical, especially for timing reasons. E.g. if an experiment runs 5 days, it doesn't matter that it doesnt use large scale resources, because realistically you need 100s of runs to evaluate a new technique and get it to the point of publishing the result, so you can only do that on a reasonable time scale if you actually have at least 10x the resources needed to run it.

Sorry, slightly off topic, but it's becoming a more and more salient point from the point of academic RL users.