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HTMuon: Improving Muon via Heavy-Tailed Spectral Correction
arXiv – CS AI|Tianyu Pang, Yujie Fang, Zihang Liu, Shenyang Deng, Lei Hsiung, Shuhua Yu, Yaoqing Yang|
🤖AI Summary
Researchers have developed HTMuon, an improved optimization algorithm for training large language models that builds upon the existing Muon optimizer. HTMuon addresses limitations in Muon's weight spectra by incorporating heavy-tailed spectral corrections, showing up to 0.98 perplexity reduction in LLaMA pretraining experiments.
Key Takeaways
- →HTMuon improves upon the Muon optimizer by addressing its suppression of heavy-tailed weight spectra in neural network training.
- →The algorithm is motivated by Heavy-Tailed Self-Regularization theory and can work as a plug-in enhancement for existing Muon variants.
- →Experiments show consistent performance improvements in both LLM pretraining and image classification tasks.
- →HTMuon achieved up to 0.98 perplexity reduction compared to standard Muon in LLaMA pretraining on C4 dataset.
- →The researchers provide theoretical analysis showing HTMuon corresponds to steepest descent under Schatten-q norm constraints.
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#htmuon#llm-training#optimization#neural-networks#machine-learning#research#muon-optimizer#heavy-tailed#llama#pretraining
Read Original →via arXiv – CS AI
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