Gradient Approximation

Gradient approximation focuses on efficiently estimating gradients, crucial for optimizing complex models in various fields, particularly deep learning and reinforcement learning. Current research emphasizes developing accurate yet computationally inexpensive gradient approximations within diverse architectures, including graph neural networks, actor-critic methods, and large language models, often employing techniques like stochastic gradient descent, low-rank approximations, and interpolation methods. These advancements improve training efficiency and performance in applications ranging from control systems and robotics to natural language processing and 3D reconstruction, addressing challenges like oversmoothing, gradient explosion, and the high computational cost of training large models.