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Robustness and Regularization in Hierarchical Re-Basin
Benedikt Fra · 2026-05-20 · via cs.LG updates on arXiv.org

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Abstract:This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme that significantly outperforms the standard MergeMany algorithm. With our new algorithm, we find that Re-Basin induces adversarial and perturbation robustness into the merged models, with the effect becoming stronger the more models participate in the hierarchical merging scheme. However, in our experiments Re-Basin induces a much bigger performance drop than reported by the original authors.
Comments: Published in 32th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2510.09174 [cs.LG]
  (or arXiv:2510.09174v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2510.09174

arXiv-issued DOI via DataCite

Related DOI: https://doi.org/10.14428/esann/2024.ES2024-22

DOI(s) linking to related resources

Submission history

From: Benedikt Franke [view email]
[v1] Fri, 10 Oct 2025 09:17:10 UTC (234 KB)
[v2] Mon, 13 Oct 2025 11:42:48 UTC (234 KB)
[v3] Tue, 19 May 2026 12:27:39 UTC (233 KB)