New Muon-Style Optimizers Improve Deep Learning Training Stability
Key takeaways
- The geometric representation of parameters significantly impacts deep learning optimizer performance.
- Muse optimizers offer a unified framework for exploring different parameter representations.
- Balanced non-native representations can achieve strong performance in large language models.
- Understanding optimizer geometry is crucial for stable and efficient training of complex AI models.
Who benefits
Summary
This research introduces Muse, a family of Muon-style optimizers that enhance training stability and performance across various parameter representations in deep learning models. It explores how different matrix representations influence optimizer geometry and convergence, showing that balanced non-native representations can match or exceed native performance.
Why it matters
Professionals working with large-scale deep learning models can leverage these insights to select or design more effective optimizers, potentially leading to faster training, better convergence, and improved model performance. Understanding optimizer geometry can help mitigate issues like vanishing/exploding gradients.
How to implement this in your domain
- 1Evaluate existing optimizer choices for large language models, considering their underlying parameter representations.
- 2Experiment with different parameter representations (e.g., non-native, balanced) in Muon-style optimizers to observe performance impacts.
- 3Integrate the Muse framework or similar geometric principles into custom optimizer development for specific model architectures.
- 4Monitor convergence metrics and gradient behavior when adjusting representation choices to identify optimal configurations.
Original post by Da Chang, Qiankun Shi, Lvgang Zhang, Di He, Yaoshuai Ma, Ganzhao Yuan, Yongxiang Liu
"arXiv:2607.14536v1 Announce Type: new Abstract: Muon-style optimizers apply a polar map to matrix momentum, but their updates also depend on the representation of each parameter block before orthogonalization. We study this representation choice as a form of optimizer geometry an…"
View on XOriginally posted by Da Chang, Qiankun Shi, Lvgang Zhang, Di He, Yaoshuai Ma, Ganzhao Yuan, Yongxiang Liu on X · view source
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