Model Soups: Averaging Fine-Tuned Weights Beats Ensembles' Cost
Wortsman, Ilharco, Gadre, Roelofs, Gontijo-Lopes, Morcos, Namkoong, Farhadi, Carmon, Kornblith, Schmidt · ICML 2022
arXiv 2203.05482
Interactive Visualization
The recipe: sweep, then average
Click a step to highlight it. Every run starts from the same pre-trained weights.
Why averaging weights is risky
Loss plane through weight space Illustrative schematic
Loss along the straight line A→B Illustrative
Greedy soup, step by step Illustrative numbers
Sort by validation accuracy, add a checkpoint, keep it only if validation accuracy doesn't drop. Accuracies below are made up to show the logic.
Soup variants compared
How big is the ViT-G gain? 10,000 test images
Each cell is one image out of 10,000. Highlighted count is the reported gap; cell positions are Illustrative
Open questions