Comparison
Backpropagation vs Pretraining
Backpropagation and Pretraining are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Backpropagation
Backpropagation comes up when the question is fundamentally about training.
PyTorch's loss.backward() triggers backpropagation.
When you would reach for Pretraining
Pretraining comes up when the question is fundamentally about training.
GPT-3 pretrained on ~300B tokens.
Frequently asked
What is the difference between Backpropagation and Pretraining?
Backpropagation: Backpropagation is the algorithm used to compute how each weight in a neural network should change to reduce error, by propagating gradients backward through the network. Pretraining: Pretraining is the initial training phase where an LLM learns to predict the next token on trillions of tokens of general text. It produces a base model that can be adapted later.
When should I use Backpropagation vs Pretraining?
Backpropagation is the right concept when you are focused on training. Pretraining applies when you are focused on training.
Are Backpropagation and Pretraining the same thing?
No. Backpropagation is training; Pretraining is training. They are related but address different parts of the AI stack.