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Model Soups: Why Averaging Fine-Tuned Weights Beats Picking the Best One

Fine-tuning usually ends with a choice. You train several versions of a model, with different seeds, learning rates, or data orders, evaluate them all, and keep the best one. The rest are discarded. Model soups challenge that habit. Instead of picking a single winner, you average the weights of sev…

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  1. 2026-11-03 15:20 · DEV Community — Machine Learning
    Model Soups: Why Averaging Fine-Tuned Weights Beats Picking the Best One

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