Model Merging with Functional Dual Anchors

Kexuan Shi, Yandong Wen, Weiyang Liu

Model Merging with Functional Dual Anchors: 12 upvotes on Hugging Face Daily Papers, #12 of 26 papers on 2025-10-27. Day-by-day upvote history.

Model merging is an efficient post-training strategy for integrating knowledge from multiple finetuned checkpoints of a shared foundation model. Existing methods operate in the parameter space, combining task vectors to mitigate conflicts, but remain constrained by parameter inconsistencies. We propose Functional Dual Anchors (FDAs), a framework that instead models the input-representation space. FDAs are synthetic inputs whose induced gradients align with task vectors, capturing task-specific functional shifts relative to the pretrained model. This perspective bridges joint multi-task training and post-hoc merging, offering both robustness and flexibility. We further introduce a principled initialization scheme and show that FDAs are complementary to parameter-space model merging. Comprehensive experiments demonstrate the effectiveness of FDAs in model merging.

Paper page on Hugging Face · arXiv

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