World models stop training a controller separately: LeWAM folds action prediction into one pass, lifting contact-rich success from 28.6% to 89.7%
LeWAM trains visual representation, action-conditioned latent dynamics, and a stochastic policy in a single stage, raising planning success from 28.6% to 89.7% on contact-rich tasks and from 10.9% to 46.2% on long-horizon tasks while planning 32.3 times faster.