Model soups need only one ingredient

• URL: http://arxiv.org/abs/2602.09689v1
• Date: Tue, 10 Feb 2026 11:44:19 GMT
• Title: Model soups need only one ingredient
• Authors: Alireza Abdollahpoorrostam, Nikolaos Dimitriadis, Adam Hazimeh, Pascal Frossard,
• Abstract summary: Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of robustness.<n>Weight-space ensembling methods, such as Model Soups, mitigate this effect by averaging multiple checkpoints.<n>We introduce MonoSoup, a simple, data-free, hyper parameter-free, post-hoc method that achieves a strong ID-OOD balance using only a single checkpoint.
• Score: 34.18140086731622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of out-of-distribution (OOD) robustness as representations specialize to the fine-tuning data. Weight-space ensembling methods, such as Model Soups, mitigate this effect by averaging multiple checkpoints, but they are computationally prohibitive, requiring the training and storage of dozens of fine-tuned models. In this paper, we introduce MonoSoup, a simple, data-free, hyperparameter-free, post-hoc method that achieves a strong ID-OOD balance using only a single checkpoint. Our method applies Singular Value Decomposition (SVD) to each layer's update and decomposes it into high-energy directions that capture task-specific adaptation and low-energy directions that introduce noise but may still encode residual signals useful for robustness. MonoSoup then uses entropy-based effective rank to automatically re-weigh these components with layer-wise coefficients that account for the spectral and geometric structure of the model. Experiments on CLIP models fine-tuned on ImageNet and evaluated under natural distribution shifts, as well as on Qwen language models tested on mathematical reasoning and multiple-choice benchmarks, show that this plug-and-play approach is a practical and effective alternative to multi-checkpoint methods, retaining much of their benefits without their computational overhead.

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