Advanced Optimizers Boost Large LLM Pretraining Efficiency
Summary
This work adapts and enhances higher-order optimizers like SOAP and Muon to overcome stability and computational challenges in large-scale LLM pretraining, demonstrating their superior performance over AdamW at multi-billion-parameter scales and large batch sizes. The researchers also introduce a layer-wise distributed optimizer and system-level improvements, releasing a codebase for emerging optimization algorithms.
Why it matters
For organizations developing and training large language models, these advancements offer a path to significantly reduce training time and computational resources, enabling the creation of more powerful and cost-effective AI systems.
How to implement this in your domain
- 1Evaluate integrating SOAP or Muon optimizers into your LLM pretraining workflows.
- 2Experiment with the proposed algorithmic modifications for improved stability at large batch sizes.
- 3Explore the layer-wise distributed optimizer for efficient scaling of LLM training.
- 4Leverage the released codebase to accelerate research and development in advanced optimization.
Who benefits
Key takeaways
- Higher-order optimizers like SOAP and Muon outperform AdamW for large LLM pretraining.
- Algorithmic modifications improve stability at large batch sizes, preventing loss spikes.
- A new layer-wise distributed optimizer enables efficient scaling of these methods.
- These advancements can significantly reduce LLM training time and cost.
Original post by Mikail Khona, Aditya Vavre, Boxiang Wang, Deyu Fu, Hao Wu, Mike Chrzanowski, Bryan Catanzaro, Dheevatsa Mudigere, Jeff Pool, Michael Lightstone, Mohammad Shoeybi, Mostofa Patwary, Nima Tajbakhsh, Tijmen Blankevoort
"arXiv:2607.20548v1 Announce Type: new Abstract: Higher-order optimizers such as Muon and SOAP offer faster convergence than AdamW, but their computational cost and numerical stability challenges have limited adoption at scale. In this work, we adapt and enhance preconditioned gra…"
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Originally posted by Mikail Khona, Aditya Vavre, Boxiang Wang, Deyu Fu, Hao Wu, Mike Chrzanowski, Bryan Catanzaro, Dheevatsa Mudigere, Jeff Pool, Michael Lightstone, Mohammad Shoeybi, Mostofa Patwary, Nima Tajbakhsh, Tijmen Blankevoort on X · view source
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