RMSprop is an adaptive learning rate optimization algorithm used in training neural networks. It helps improve the convergence speed by adjusting the learning rate for each parameter based on the average of recent gradients. This means that parameters with larger gradients will have their learning rates reduced, while those with smaller gradients will have their learning rates increased. Developed by Geoff Hinton, RMSprop is particularly effective for non-stationary objectives, making it suitable for tasks like deep learning. By maintaining a moving average of squared gradients, it helps to stabilize the updates, allowing for more efficient training and better performance in various applications.