Summary of Subspace Optimization For Large Language Models with Convergence Guarantees, by Yutong He et al.
Subspace Optimization for Large Language Models with Convergence Guarantees
by Yutong He, Pengrui Li, Yipeng Hu, Chuyan Chen, Kun Yuan
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 GaLore, a subspace optimization algorithm, has been widely used for pre-training and fine-tuning large language models (LLMs) due to its memory efficiency. However, the convergence guarantees of GaLore remain unclear, particularly in stochastic settings. This paper investigates the conditions under which GaLore can achieve convergence, demonstrating that it converges either in deterministic scenarios or with a sufficiently large mini-batch size. A novel variant, GoLore, is introduced, which provably converges in stochastic settings even with standard batch sizes. Theoretical results are validated through numerical experiments and empirical exploration of the proposed mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GaLore helps computers learn faster by using less memory. But does it always work? This paper found that GaLore doesn’t always get to the best answer. They looked at what makes it work and when it fails. Then, they created a new way called GoLore that always works. The researchers tested their ideas and showed that GoLore is better. |
Keywords
» Artificial intelligence » Fine tuning » Optimization
