Comparison
Fine-tuning vs Learning Rate
Fine-tuning 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 Fine-tuning
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples.
Fine-tuning Llama 3 on medical Q&A for a clinical assistant.
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 Fine-tuning and Learning Rate?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. 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 Fine-tuning vs Learning Rate?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. Learning Rate applies when you are focused on training.
Are Fine-tuning and Learning Rate the same thing?
No. Fine-tuning is training; Learning Rate is training. They are related but address different parts of the AI stack.