What is the learning rate in training?

The learning rate is a hyperparameter that determines how much a machine learning model adjusts its parameters during training. When a model learns from data, it uses an optimization algorithm like gradient descent to minimize errors. The learning rate controls the size of the steps the algorithm takes toward the minimum error. If the rate is too high, the model might overshoot the optimal solution, causing unstable training or divergence. If it’s too low, the model might learn too slowly, requiring more time or computational resources to converge. Balancing this value is critical because it directly impacts training efficiency and final performance.

For example, consider training a neural network using stochastic gradient descent (SGD). Suppose the learning rate is set to 0.1. During each update, the model calculates the gradient of the loss with respect to its parameters and multiplies it by 0.1 to adjust the weights. A high rate like 0.5 might cause the weights to oscillate around the optimal values without settling, while a low rate like 0.001 might result in minimal progress over hundreds of epochs. Adaptive optimizers like Adam or RMSProp address this by automatically adjusting the learning rate for each parameter, often starting with a default value like 0.001. However, even with adaptive methods, initial rate selection remains important—starting with a poorly chosen value can still slow convergence.

Choosing the right learning rate often involves experimentation. Developers might use techniques like learning rate schedules, which reduce the rate over time (e.g., starting at 0.1 and decaying by 10% each epoch). Another approach is a learning rate finder: training the model with a gradually increasing rate to identify the value where loss decreases fastest. For instance, in PyTorch, libraries like torch.optim.lr_scheduler provide built-in tools for implementing schedules. Additionally, pretraining phases (like warmup periods in transformer models) start with a low rate to stabilize early updates before ramping up. Ultimately, the ideal rate depends on factors like model architecture, dataset size, and task complexity, making systematic testing essential for tuning.