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technologyreview.com·1h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Muon Learns More Robust and Transferable Features than Adam
TL;DR
Existing optimizers like Adam and SGD have limitations in feature learning for Large Language Models (LLMs) and vision classifiers. Muon, a new optimizer, was evaluated for its ability to learn more robust and transferable features.
✦ Why It Matters
Engineers can leverage Muon to improve the robustness of models in real-world applications with noisy data.
Key Takeaways
- Consider using Muon for training LLMs and vision classifiers.
- Evaluate model robustness on corrupted data to ensure reliability.
- Explore the implications of feature transferability in diverse applications.
- Stay updated on advancements in optimizers for better model performance.
Full Summary
Optimizers are crucial for training machine learning models, particularly Large Language Models (LLMs) and vision classifiers. Muon has been introduced as a state-of-the-art optimizer, but its advantages in feature learning were not well understood.
This study evaluated Muon's performance by comparing it to Adam and Stochastic Gradient Descent (SGD) on pretrained models subjected to corrupted inputs. The methodology involved assessing the robustness of features learned by each optimizer through tests on corrupted images and texts.
Findings revealed that features learned by Muon consistently outperformed those learned by Adam and SGD, demonstrating greater resilience to input corruption. These results suggest that Muon not only enhances training efficiency but also improves the quality of learned features, making it a valuable tool for engineers and researchers in AI.