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    Home»Development»Machine Learning»Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging

    Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging

    July 4, 2025

    Large-scale models are routinely trained on a mixture of different data sources.
    Different data mixtures yield very different downstream performances.
    We propose a novel architecture that can instantiate one model for each data mixture without having to re-train the model.
    Our architecture consists of a bank of expert weights, which are linearly combined to instantiate one model.
    We learn the linear combination coefficients as a function of the input histogram.
    To train this architecture, we sample random histograms, instantiate the corresponding model, and backprop through one batch of data…

    Source: Read MoreÂ

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