Model Merging by Uncertainty-Based Gradient Matching

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. The connection also reveals implicit assumptions in other schemes such as averaging, task arithmetic, and Fisher-weighted averaging. Our new method gives consistent improvements for large language models and vision transformers, both in terms of performance and robustness to hyperparameters.

Authors

• Nico Daheim
• Thomas Möllenhoff
• Edoardo M. Ponti
• Iryna Gurevych
• Mohammad Emtiyaz Khan

Keywords

• Model Merging
• Gradient Matching
• Language Modeling
• Model Editing
• Transfer Learning