Summary of Copra: a Progressive Lora Training Strategy, by Zhan Zhuang et al.
CopRA: A Progressive LoRA Training Strategy
by Zhan Zhuang, Xiequn Wang, Yulong Zhang, Wei Li, Yu Zhang, Ying Wei
First submitted to arxiv on: 30 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 Low-Rank Adaptation (LoRA) is a method for fine-tuning foundation models efficiently. Traditional LoRA training often converges to a local optimum early on, which may not be ideal for out-of-distribution data or tasks like merging and pruning. This paper proposes a novel progressive training strategy called Cooperative LoRA (CopRA), which optimizes the Shapley value of LoRA parameters in each layer using random layer dropping. CopRA enables efficient model merging, paving the way for federated learning and multi-task learning via LoRA merging. Additionally, CopRA’s optimized Shapley value leads to superior performance in pruning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make computer models better at adapting to new information. The method is called LoRA, which stands for Low-Rank Adaptation. Normally, when you fine-tune these models, they get stuck in a bad spot early on. This new approach, called CopRA (Cooperative LoRA), helps the model find better solutions by looking at each part of the model separately and optimizing how well it works together. This makes it easier to combine different models or work with limited data. The results show that this method is really good at trimming down large models while keeping their performance strong. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Lora » Low rank adaptation » Multi task » Pruning
