What is the vanishing gradient problem in deep learning, and how does it occur during training? How does this issue affect the learning process of deep neural networks? In which types of neural network architectures is the vanishing gradient problem most common? What techniques are used to overcome or reduce this problem? How does solving the vanishing gradient problem improve model performance and training efficiency?