Training Dynamic

Training dynamics in neural networks investigates how model parameters evolve during training, aiming to understand and optimize the learning process for improved performance and efficiency. Current research focuses on characterizing training dynamics across various architectures, including transformers and convolutional networks, using techniques like contrastive learning and analyzing loss landscapes to identify optimal training protocols and mitigate issues like catastrophic forgetting and reward hacking in reinforcement learning from human feedback (RLHF). These studies are crucial for developing more efficient training methods, improving model generalization, and ultimately advancing the capabilities of artificial intelligence across diverse applications.