Summary of Addax: Utilizing Zeroth-order Gradients to Improve Memory Efficiency and Performance Of Sgd For Fine-tuning Language Models, by Zeman Li et al.
Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models
by Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 This paper proposes Addax, a novel optimization method that addresses the limitations of existing methods for fine-tuning language models (LMs). The Adam optimizer is often used for LMs, but it demands excessive memory, making it inaccessible. The in-place version of Stochastic Gradient Descent (IP-SGD) and Memory-Efficient Zeroth-order Optimizer (MeZO) have been proposed to mitigate this issue, but they suffer from slow convergence or degraded final performance. Addax integrates IP-SGD with MeZO by computing zeroth- or first-order gradients based on memory consumption and combining these estimates to update directions. This approach overcomes the limitations of existing methods, achieving faster convergence and better final performance while using comparable memory. The paper theoretically establishes the convergence of Addax under mild assumptions and demonstrates its effectiveness through experiments with diverse LMs and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to make computers learn from large language models without using too much memory. Currently, this process is limited by how much memory is available. The authors suggest a new method called Addax that can fine-tune language models quickly and accurately while using the same amount of memory as other methods. This is important because it allows more people to use these powerful language models for different tasks. The paper shows that Addax works well in various situations and performs better than existing methods. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Stochastic gradient descent
