Comparison
Learning Rate vs Loss Function
Learning Rate and Loss Function are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
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.
When you would reach for Loss Function
Loss Function comes up when the question is fundamentally about training.
Cross-entropy loss in next-token prediction.
Frequently asked
What is the difference between Learning Rate and Loss Function?
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. Loss Function: A loss function measures how wrong a model's predictions are. Training minimizes it. For LLMs the loss is the cross-entropy of predicted vs. actual next tokens.
When should I use Learning Rate vs Loss Function?
Learning Rate is the right concept when you are focused on training. Loss Function applies when you are focused on training.
Are Learning Rate and Loss Function the same thing?
No. Learning Rate is training; Loss Function is training. They are related but address different parts of the AI stack.