Historically, R&D has relied on near-frontier-scale experiments, including major training runs that require decisions about what to include.

“basically like historically R&D has been driven by doing near frontier scale experiments and that has been pretty important and like actually doing the one big training run where you decide exactly what to include in that and there's a bunch of ways that the AIs can sort of make that more verifiable so they can have better science of exactly what to predict they can scale down their frontier scale training runs to a point where they can study that scale more aggressively at some one-time hit to compute cost right so like if people wanted to”