Understanding Its Purpose
Backpropagation computes derivatives backward through the computational process, determining how each parameter contributes to changes in the loss.
Understanding Through an Example
A prediction passes through many layers of computation before the loss is obtained. Backpropagation starts from the loss and works backward layer by layer, calculating: if we slightly change this value here, how will the final loss change? It provides the basis for adjustment but does not itself "update parameters."
Input passes through multiple layers to obtain loss
Use chain rule to pass through layers
Passed to the optimizer
Going a Layer Deeper
A complex computation consists of many smaller operations. The chain rule links together the derivatives of these smaller operations to obtain the derivative of the loss with respect to the parameters. Automatic differentiation frameworks handle the bookkeeping of computational dependencies, with backpropagation being a common method for computing gradients.
What It Doesn't Do
Computing gradients and updating parameters are two separate steps. Backpropagation is also not reading generated answers in reverse, nor is it a reward model scoring answers. If certain parameters are frozen, they won't be updated in this training round.
Where to Learn More
References
- PyTorch · Automatic Differentiation: Backpropagation computes parameter gradients using the chain rule, separate from optimizer updates.
Information verified on 2026-09-09; original papers are cited to explain mechanisms; examples in the text are for instructional purposes.