What Is Weight Averaging? A Simple Form of Model Merging
The simplest possible way to combine multiple trained models: literally average their corresponding parameter values together.
Literally averaging parameter values together
Weight averaging combines multiple trained models by literally averaging their corresponding parameter values together, position by position — the simplest possible form of model merging, requiring no additional training and only basic arithmetic across the source models' parameters.
Why something this simple can actually work
When models being averaged started from the same base and were trained similarly, their parameters tend to occupy a similar region of the model's parameter space, which is exactly the condition under which simple averaging tends to produce a coherent, functional result rather than a broken, meaningless mixture.
A well-known specific use case
Averaging the weights of several checkpoints taken from different late points in the same training run — sometimes called "model souping" — is a well-known specific application, often producing a slightly more robust final model than any single one of the individual checkpoints being averaged.
Frequently Asked Questions
What is weight averaging?
A technique that combines multiple trained models by literally averaging their corresponding parameter values together — the simplest possible form of model merging, requiring no additional training.
When does weight averaging tend to work well?
When the models being averaged started from the same base and were trained similarly, so their parameters occupy a similar region of the model's parameter space — a condition under which simple averaging produces a coherent result.