Second Order

Second-order methods in machine learning leverage curvature information, primarily through Hessian matrices or their approximations, to improve optimization efficiency and model performance compared to first-order methods. Current research focuses on developing computationally tractable second-order algorithms, such as those employing diagonal Hessian approximations or low-rank matrix factorizations, for training large-scale models like LLMs and improving reinforcement learning. These advancements are significant because they offer faster convergence, enhanced generalization, and improved robustness in various applications, including image classification, natural language processing, and robotics.