Worth noting that the LeWM paper (Maes, Le Lidec, Scieur, LeCun, Balestriero) is more interesting for its training method than the Mario demo. The key contri

Worth noting that the LeWM paper (Maes, Le Lidec, Scieur, LeCun, Balestriero) is more interesting for its training method than the Mario demo. The key contribution is SIGReg — a single regularization term that prevents representation collapse in JEPA architectures, replacing the 6+ hyperparameters you'd need with VICReg or Barlow Twins. The architecture itself is a ViT encoder producing a single CLS token per frame, feeding into a transformer predictor with AdaLN-Zero conditioning on actions. Everything happens in a ~192-D latent space. A 15M parameter model trains on a single GPU in minutes, which is a very different regime from Dreamer-v3 or TD-MPC2. The Mario demo is flashy but the real test is whether the learned latent space is structured enough for downstream planning. The paper shows CEM planning working on Push-T manipulation, which is more practically relevant than game environments.