Core Insights - The article emphasizes that publishing papers is no longer the ultimate goal for researchers, as demonstrated by Keller Jordan's success with a blog post that led to his position at OpenAI [2][8]. - The case of Keller Jordan illustrates that talent acquisition in top AI research institutions like OpenAI prioritizes capability over traditional academic metrics [8]. Summary by Sections Blog Post Overview - Keller Jordan's blog titled "Muon: An optimizer for hidden layers in neural networks" was published on December 8, 2024, and introduced a new optimizer that significantly enhances training speed while maintaining accuracy for neural networks [4][6]. - The blog highlights the latest records in training speed for NanoGPT, with the most recent record being 2.979 minutes achieved on May 25, 2025 [9]. Muon Optimizer Design and Results - Muon is designed to optimize hidden layers in neural networks, achieving a training speed record of 2.6 seconds on the CIFAR-10 dataset while maintaining 94% accuracy [22]. - In competitive tasks, Muon demonstrated a 1.35 times improvement in training speed compared to previous methods [22]. - The optimizer's design involves applying Newton-Schulz iterations to approximate orthogonal updates, which enhances the learning process by diversifying update directions [29][30]. Performance and Efficiency - Muon requires minimal additional computational overhead, with a maximum FLOP cost of less than 1% in typical language model training scenarios [58][59]. - The optimizer has shown superior performance in training large models, such as a 1.5 billion parameter Transformer, compared to traditional methods like AdamW [22][66]. Comparison with Other Optimizers - The article discusses the limitations of other optimizers, such as Shampoo and Orthogonal-SGDM, highlighting that Muon outperforms them in efficiency and effectiveness [61][64]. - It emphasizes the importance of proper baseline tuning in research to ensure that new optimizers are genuinely effective [72]. Future Research Directions - The article mentions ongoing research to explore Muon's scalability and its application in various training scenarios, indicating a growing interest in its potential [79][81].