Generalized Linear Mode Connectivity for Transformers

NeurIPSOral2025

Authors
Alexander Theus, Alessandro Cabodi, Sotiris Anagnostidis, Antonio Orvieto, Sidak Pal Singh, Valentina Boeva
Affiliation
ETHZ - ETH Zurich
Venue
NeurIPS 2025
Track
Oral

TL;DR

We propose a unified framework for model merging that leverages multiple symmetry classes to enable low- and zero-loss interpolation between independently trained Transformer models, including Vision Transformers and GPT-2.

Opening excerpt from the authors’ abstract. source

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Topics

transformer rag

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