Summary of The Non-local Model Merging Problem: Permutation Symmetries and Variance Collapse, by Ekansh Sharma et al.
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse
by Ekansh Sharma, Daniel M. Roy, Gintare Karolina Dziugaite
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to combine multiple expert models trained on different tasks into a single multi-task model. The goal is to achieve strong performance across all tasks without requiring extensive fine-tuning. Existing methods for combining expert models have been successful when the expert models are “local” neighbors of each other, meaning they share a common foundation model and have similar weights. However, this paper explores the more challenging scenario where expert models are significantly different from each other, making it difficult to combine them efficiently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to merge multiple expert models into one powerful multi-task model that performs well on many tasks at once. Right now, there are ways to do this, but they work best when the expert models are similar and share a common foundation. This paper looks at what happens when the expert models are very different from each other. |
Keywords
» Artificial intelligence » Fine tuning » Multi task
