this is exactly what Slowrun aims to solve. i think the main problem is not the loss function but the learning algorithm:- gradient descent massively underutilizes compute relative to the data it's trained on. i think brains use a lot of compute to search for solutions that generalize, rather than doing computationally cheap but unexploratory curve fitting.- we've already gotten 10x better sample efficiency with Slowrun by pulling on two levers that let us utilize a lot of compute: heavy overpar