Muon Explained: Adam's First Real Challenger

AdamW has been the default optimizer for training large neural networks for nearly a decade. But a new optimizer called Muon may be its first serious challenger. In this video, we visually explain how Muon optimizer works, why it is different from Adam and AdamW, and why researchers are paying attention to it for large-scale LLM training. Instead of treating every weight independently, Muon looks at weight matrices geometrically. It orthogonalizes the momentum update, reshapes the singular value spectrum, and pushes training updates across more useful directions. We’ll break down the core idea behind momentum orthogonalization, Newton-Schulz iteration, polar factors, and why Muon can be more compute-efficient than AdamW. We’ll also explain the catch: why vanilla Muon can destabilize attention layers at frontier scale, and how QK-Clip turns it into MuonClip, making it more stable for large language model training. Topics covered: Why AdamW became the default optimizer Adam’s blind spot with matrix weights Singular values, SVD, and matrix geometry How Muon orthogonalizes momentum updates Newton-Schulz iteration explained visually Why Muon can reduce training compute Why attention logits can explode with Muon QK-Clip and MuonClip explained Why Muon matters for future LLM training If you’re interested in LLM training, optimizers, transformer architecture, AdamW, Muon, Newton-Schulz, scaling laws, and frontier AI training, this video gives you a visual explanation of one of the most interesting optimizer ideas in modern deep learning. Muon optimizer Muon explained Muon optimizer explained AdamW vs Muon Adam vs Muon Adam optimizer AdamW optimizer LLM optimizer LLM training optimizer deep learning optimizer neural network optimizer AI optimizer transformer optimizer training large language models LLM training large language model training how LLMs are trained optimizer explained AdamW explained MuonClip MuonClip explained QK-Clip QK Clip explained attention logits attention instability frontier LLM training Kimi K2 Muon Moonlight Muon Moonshot AI Muon Newton Schulz iteration Newton-Schulz iteration momentum orthogonalization orthogonalized momentum polar factor singular value decomposition SVD explained singular values matrix geometry matrix weights fast LLM training compute efficient training training compute scaling laws LLM scaling laws Pareto frontier AI Megatron Core Muon NVIDIA Megatron Core transformer training deep learning explained machine learning explained AI research explained large language models explained modern AI training future of LLMs Adam’s blind spot Muon vs AdamW optimizer for transformers training at scale LLM training stability loss spikes query key clipping QK norm multi head latent attention MLA attention frontier AI models AI training explained