The potential of a one-model end-to-end solution

Although the One Model end-to-end solution is more complex in training and debugging, its theoretical upper limit of effectiveness is higher. Whether using a learning architecture based on RL/IL or a derivative architecture based on a world model, the One Model end-to-end solution can cover a wider range of data and achieve better generalization effects.