arXiv:2605.26693v1 Announce Type: new Abstract: Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the geometry of the loss landscape or rely on intractable full-space Hessian approximations. We propose EpiMer, a framework that casts model merging as solving the Fr\'echet mean on a Riemannian manifold and restricts the computation to a low-rank subspace spanned by the task vectors. With the expected Hessian as the metric, we reveal a connection between local curvature and epistemic uncertainty of th
The proliferation of various AI models necessitates more efficient methods for their combination and optimization without costly retraining, making research into model merging geometries particularly timely.
Improving model merging efficiency and effectiveness can accelerate AI development, reduce computational costs, and enhance the integration of diverse AI capabilities across applications.
The proposed EpiMer framework provides a more geometrically informed approach to model merging, potentially leading to more robust and performant integrated AI systems.
- · AI researchers
- · Cloud computing providers
- · Generative AI companies
- · Machine learning platform developers
- · Companies reliant on single, large retraining cycles
More efficient integration of specialized AI models becomes possible, fostering modular AI development.
This could lead to a 'lego-block' approach to AI, where distinct models are combined for novel applications with reduced overhead.
Accelerated AI development could further intensify the demand for compute, while simultaneously making model deployment more flexible.
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Read at arXiv cs.LG