Explorative Modeling pretrains generative models 4.1× faster by exploring K candidates per step
Explorative Modeling, a preprint from researchers at Cornell and MIT, factors the training loop instead of the generation procedure, exploring multiple candidate outputs and training on the best match to improve FLOP efficiency by 4.1× and sample efficiency by 6.2×.










