Comparison
Gradient Descent vs Learning Rate
Gradient Descent and Learning Rate are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Gradient Descent
Gradient Descent comes up when the question is fundamentally about training.
A linear regression model learning the slope and intercept.
When you would reach for Learning Rate
Learning Rate comes up when the question is fundamentally about training.
Pretraining: peak LR around 1e-4 with cosine decay.
Frequently asked
What is the difference between Gradient Descent and Learning Rate?
Gradient Descent: Gradient descent is the optimization algorithm at the heart of training: nudge each weight in the direction that reduces the loss, with a small step size set by the learning rate. Learning Rate: The learning rate is the step size used to update weights during training. Too high and training diverges; too low and it crawls or gets stuck.
When should I use Gradient Descent vs Learning Rate?
Gradient Descent is the right concept when you are focused on training. Learning Rate applies when you are focused on training.
Are Gradient Descent and Learning Rate the same thing?
No. Gradient Descent is training; Learning Rate is training. They are related but address different parts of the AI stack.