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MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training

arXiv — cs.LG•Wednesday, May 27, 2026 at 4:00:00 AM
PositiveArtificial Intelligence
  • What Happened

    The Muon optimizer has been enhanced with the introduction of MONA, which integrates Nesterov acceleration to improve convergence and performance in large language model training. This new optimizer addresses the limitations of Muon, particularly its tendency to become trapped in sharp local minima, by incorporating an acceleration term derived from the exponential moving average of gradient differences.

  • Why It Matters

    MONA's development is significant as it demonstrates a substantial improvement over both Muon and AdamW optimizers, achieving better convergence and performance across various scales of Mixture-of-Experts pretraining, which is crucial for advancing the efficiency of language model training.

  • The Bigger Picture

    The introduction of MONA reflects a broader trend in AI optimization, where researchers are increasingly focusing on enhancing existing frameworks like Muon. This includes various adaptations and new optimizers, such as TrasMuon and AMUSE, which aim to stabilize training processes and improve performance, indicating a growing emphasis on refining optimization techniques to meet the demands of complex AI models.

— via World Pulse Now AI Editorial System
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