Model Merging on Loss Landscape: A Geometry Perspective 事件

Model Merging on Loss Landscape: A Geometry Perspective arXiv:2605.26693v1 Announce Type: cross 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-ran