DOGE merges fine-tuned models by optimizing a data-free loss-gap proxy with gradient steps projected orthogonal to a shared task subspace, improving average accuracy over previous methods.
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Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent
DOGE merges fine-tuned models by optimizing a data-free loss-gap proxy with gradient steps projected orthogonal to a shared task subspace, improving average accuracy over previous methods.