New sMuon method enhances low-rank fine-tuning for neural networks

Researchers have developed a new method called sMuon to improve parameter-efficient fine-tuning (PEFT) of neural networks. This technique addresses the incompatibility between the Muon optimizer and the common LoRA PEFT method by approximating the Muon objective in a low-rank setting. The implementation uses only matrix multiplication operations and has shown favorable results in supervised fine-tuning and ReLoRA pretraining experiments, offering moderate performance improvements. AI

IMPACT Enhances fine-tuning efficiency for neural networks, potentially leading to better performance with fewer computational resources.

RANK_REASON The cluster contains a research paper detailing a new method for optimizing neural network fine-tuning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Hugging Face
• LoRA
• muon
• parameter-efficient fine-tuning
• ReLoRA
• sMuon
• supervised fine-tuning

• paper
• model release

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