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      "statement": "# Model soups: averaging independently fine-tuned models from a common initialization\n\nStudied uniform and validation-greedy weight averaging across independently fine-tuned models from a shared pretrained initialization, retaining a single-model inference footprint.\n\n## Scientific scope\n\nInherits weight averaging and low-error-basin observations. Fine-tuning sweeps and validation selection still cost compute; no extra inference cost does not mean free training. No guarantee for arbitrary independently initialized models, architectures, basins or every downstream task.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMitchell Wortsman; Gabriel Ilharco; Samir Yitzhak Gadre; Rebecca Roelofs; Raphael Gontijo-Lopes; Ari S. Morcos; Hongseok Namkoong; Ali Farhadi; Yair Carmon; Simon Kornblith; Ludwig Schmidt\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://arxiv.org/abs/2203.05482v1)\n",
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