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.

Inference cost: soup vs ensemble

k models: an ensemble needs k forward passes and k copies in memory. A soup is one network.

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

Reported results

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

References