No Task Left Behind: Isotropic Model Merging with Common and
  Task-Specific Subspaces
Authors: name: Daniel Marczak, name: Simone Magistri, name: Sebastian Cygert, name: Bartłomiej Twardowski, name: Andrew D. Bagdanov, name: Joost van de Weijer

Model merging integrates the weights of multiple task-specific models into a
single multi-task model. Despite recent interest in the problem, a significant
performance gap between the combined and single-task models remains. In this
paper, we investigate the key characteristics of task matrices -- weight update
matrices applied to a pre-trained model -- that enable effective merging. We
show that alignment between singular components of task-specific and merged
matrices strongly correlates with performance improvement over the pre-trained
model. Based on this, we propose an isotropic merging framework that flattens
the singular value spectrum of task matrices, enhances alignment, and reduces
the performance gap. Additionally, we incorporate both common and task-specific
subspaces to further improve alignment and performance. Our proposed approach
achieves state-of-the-art performance across multiple scenarios, including
various sets of tasks and model scales. This work advances the understanding of
model merging dynamics, offering an effective methodology to merge models
without requiring additional training. Code is available at
https://github.com/danielm1405/iso-merging .