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MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendation
arXiv – CS AI|Rong Shan, Aofan Yu, Bo Chen, Kuo Cai, Qiang Luo, Ruiming Tang, Han Li, Weiwen Liu, Weinan Zhang, Jianghao Lin||7 views
🤖AI Summary
Researchers introduce MuonRec, a new optimization framework for recommendation systems that significantly outperforms the widely-used Adam/AdamW optimizers. The framework reduces training steps by 32.4% on average while improving ranking quality by 12.6% in NDCG@10 metrics across traditional and generative recommenders.
Key Takeaways
- →MuonRec introduces the Muon optimizer to recommendation systems, using orthogonalized momentum updates for improved optimization efficiency.
- →The framework reduces converged training steps by an average of 32.4% compared to Adam/AdamW baselines.
- →MuonRec achieves consistent 12.6% relative gains in NDCG@10 ranking quality across all experimental settings.
- →The improvement is particularly pronounced in generative recommendation models compared to traditional sequential recommenders.
- →This represents the first systematic challenge to the near-universal use of Adam/AdamW optimizers in modern RecSys pipelines.
#machine-learning#optimization#recommendation-systems#muon-optimizer#adam#training-efficiency#generative-ai#recsys#research#performance
Read Original →via arXiv – CS AI
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