The neural motion simulator (MoSim) is introduced as a world model that enhances reinforcement learning by accurately predicting the future physical state of an embodied system based on current observations and actions. It enables efficient skill acquisition and facilitates zero-shot learning, allowing for a decoupling of physical environment modeling from the development of RL algorithms, thus improving sample efficiency and generalization.