Muon Can Outperform Dedicated Continual Learning Methods

arXiv:2609.24678 · cs.LG, cs.CL · Submitted 2026-09-21 · Read on arXiv

cs.LG, cs.CL

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: 10 pages, 2 figures, 6 tables. Presented at the 5th Conference on Lifelong Learning Agents (CoLLAs), Work-in-Progress Track, 2026. Sebastian George Sincari and Bogdan Alexandru Gheorghe contributed equally

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 31× in this paper · explained in 2 episodes
• Benchmark — 26× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Continual Learning — 6× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 1× in this paper
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective
• Muon is Scalable for LLM Training
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

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