LaLa-World: Scaling latent world models with natural language actions

The paper proposes LaLa-World, a language-conditioned latent world model (LWM) that learns jointly from simulation and robot trajectories as well as broader image and video data. Its key design is to describe both numerical actions and visual changes in language, enabling training of a single future predictor across heterogeneous datasets, and to build the predictor on a language model so it can interpret language conditions.