The challenge of generalization in distributed training arises primarily due to the increased batch size, particularly when it exceeds 8,000. To address accuracy losses, momentum is employed, combining previous gradients with current ones to create a new velocity vector. This approach emphasizes accumulating velocity rather than gradients, enhancing the effectiveness of gradient descent in deep neural network training.
