Summary of Revisiting Zeroth-order Optimization For Memory-efficient Llm Fine-tuning: a Benchmark, by Yihua Zhang et al.
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
by Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
First submitted to arxiv on: 18 Feb 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 The proposed paper explores the idea of using zeroth-order (ZO) optimization techniques to fine-tune Large Language Models (LLMs), aiming to reduce memory costs and make on-device training more feasible. Building upon the initial concept introduced by MeZO, the authors conduct a comprehensive benchmarking study across five LLM families, three task complexities, and five fine-tuning schemes. The study reveals novel optimization principles, highlighting the importance of task alignment, forward gradient methods, and algorithm complexity-fine-tuning performance balance. To achieve memory-efficient LLM fine-tuning, the authors introduce enhancements such as block-wise descent, hybrid training, and gradient sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to train big language models without needing so much memory. Currently, when we fine-tune these models, it takes up a lot of space because our computers need to remember all the tiny changes made during the learning process. But what if we could find a way to learn without using as much memory? This would be super helpful for devices like smartphones that don’t have lots of storage. The researchers in this paper tried different methods, found some new ideas, and even introduced some new techniques to make it all work better. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Optimization
