LRA, or Learning Rate Annealing, is a technique used in machine learning to adjust the learning rate during the training process. The learning rate determines how much the model's weights are updated in response to the error it makes. By gradually decreasing the learning rate, LRA helps the model converge more effectively, improving its performance on tasks. This method is particularly useful in optimizing complex models, such as neural networks. As training progresses, a smaller learning rate allows for finer adjustments, reducing the risk of overshooting the optimal solution. Overall, LRA enhances the stability and accuracy of the learning process.