Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space

Researchers have introduced a new framework that treats model merging as a problem of probabilistic inference rather than purely geometric manipulation. By reframing how distinct task-specific models are combined, this method offers a more rigorous foundation for creating multi-task systems without requiring extra training data.