Dynamic Model Merging Made Slim

arXiv:2605.18904v2 Announce Type: replace-cross Abstract: Model merging consolidates independently fine-tuned checkpoints without joint training or access to task data. Dynamic methods retain task-specific capacity, but must divide a limited storage budget between shared and expert parameters. We propose DiDi-Merging, which jointly allocates low-rank capacity to these components through differentiable rank selection, followed by data-free parameter-space refinement. On the eight-task ViT-B/32 benchmark, the small configuration reaches 89.3% average accuracy (98.7% normalized retention) at 1.24

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