A model should ideally be large enough to achieve a decent pass rate on the hardest training environments, but making it larger than necessary would waste inference compute.

“point where you know you can get like a decent pass at one or something on like the the hardest environments you have and like it wouldn't make sense to like make a bigger bigger model pass there because then you're just paying like much more inference That. what than you need to um so there's there's a lot of impetus like depends on how quickly you know like macore and then in-house these these guys can scale up the complexity the oral environments they're training on”