Dynamic Model Merging Made Slim

Guodong Du; Wanyu Lin

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arXiv · 2605.18904

Dynamic Model Merging Made Slim

Guodong Du·Wanyu Lin

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Abstract

Model merging enables the reuse of fine-tuned models without joint training or access to original data. Dynamic merging further improves flexibility by selectively activating task-relevant parameters and efficiently composing experts across multiple tasks. However, existing dynamic methods either maintain a full shared model with tiny experts or allocate excessive capacity to experts, leading to suboptimal accuracy--efficiency trade-offs. To address this, we propose DiDi-Merging, a slim dynamic merging framework that leverages differentiable rank allocation to balance shared and expert parameters. By formulating parameter budgeting as differentiable rank optimization in low-rank modules and introducing a data-free refinement step to recover task fidelity, DiDi-Merging matches prior dynamic baselines at only 1.24x the parameters of a single fine-tuned model and surpasses them at 1.4x, substantially more compact than methods requiring > 2x storage. DiDi-Merging applies across vision, language, and multimodal tasks.

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cs.LG cs.AI cs.CL

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Guodong Du, Wanyu Lin. 2026-05-17. Dynamic Model Merging Made Slim. https://arxiv.org/abs/2605.18904

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