RMSProp, or Root Mean Squared Propagation, is an adaptive learning rate optimization algorithm widely used in training deep learning models. It addresses the limitations of traditional gradient descent methods, particularly the issue of diminishing learning rates in algorithms like AdaGrad. By maintaining a moving average of the squared gradients, RMSProp adjusts the learning rate for each parameter dynamically, allowing for more efficient convergence. This method helps to stabilize updates, especially in scenarios with steep or narrow loss surfaces, making it a popular choice for optimizing neural networks and improving training speed and performance.