A new study explores the universality of gradient descent in training neural networks, suggesting that gradient-based optimization methods can effectively optimize a wide range of neural network architectures under certain conditions. The research, published on arXiv, delves into theoretical foundations that may help explain why gradient descent remains a dominant approach in deep learning despite the complexity and diversity of modern models. The findings could have implications for understanding convergence properties and the design of future optimization algorithms.