Summary of Deconstructing What Makes a Good Optimizer For Language Models, by Rosie Zhao et al.
Deconstructing What Makes a Good Optimizer for Language Models
by Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham Kakade
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates various optimization algorithms for training language models, with a focus on autoregressive language modeling. The authors compare five optimization methods – SGD, Adafactor, Adam, Lion, and Sophia – across different model sizes, hyperparameters, and architecture variants. The results show that all algorithms perform similarly in terms of optimal performance and stability to hyperparameter misspecification, except for SGD. The choice of optimizer can be guided by practical considerations like memory constraints and ease of implementation rather than solely focusing on performance. The authors also analyze two simplified versions of Adam: Signum and Adalayer, which recovers the performance and stability of Adam and introduces adaptivity to different layers of the network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares five optimization algorithms for training language models. It shows that all algorithms work similarly well, except for one. The choice of algorithm depends on practical things like how much memory you have and how easy it is to use. The authors also look at two simplified versions of Adam, which makes the algorithm better. |
Keywords
» Artificial intelligence » Autoregressive » Hyperparameter » Optimization
