In the context of training large language models, the learning rate is a critical hyperparameter that controls how much the model's parameters are updated in response to the estimated error each time the model's weights are updated.
Overview[edit | edit source]
The learning rate determines the step size during the stochastic gradient descent process. A high learning rate allows the model to learn faster, at the cost of possibly overshooting the minimum point of the loss function. A low learning rate allows the model to converge reliably, but at the cost of slower learning speed.
Learning Rate Scheduling[edit | edit source]
Learning rate scheduling or learning rate decay is a strategy to adjust the learning rate during the training process. The idea is to start with a relatively high learning rate to benefit from fast learning, and then reduce the learning rate as training progresses to allow the model to converge.
There are several strategies for learning rate scheduling:
- Step Decay: The learning rate is reduced by a factor after a fixed number of epochs.
- Exponential Decay: The learning rate is reduced exponentially, often after each batch or epoch.
- 1/t Decay: The learning rate is reduced as the inverse of the square root of the epoch number.
- Warm-up and Cool-down periods: The learning rate is initially increased for a certain number of epochs (warm-up), and then gradually decreased towards the end of training (cool-down).
The choice of learning rate and schedule can greatly impact the quality of the final model and is often determined through experimentation and Hyperparameter tuning.