AdamW, Muon, and ROOT: Introducing ROOT, a Robust Orthogonalized Optimizer for Neural Network Training

This article introduces the ROOT optimizer, presented in the paper "ROOT: Robust Orthogonalized Optimizer for Neural Network Training." The article highlights the problem of instability often encountered during the training of large language models (LLMs) and suggests that the design of the optimization algorithm itself is a contributing factor. While the article is brief, it points to a potentially significant advancement in optimizer design for LLMs, addressing a critical challenge in the field. Further investigation into the ROOT algorithm's performance and implementation details would be beneficial to fully assess its impact.

• •Introduces the ROOT optimizer for neural network training.
• •Addresses the instability issues in LLM training.
• •Suggests optimization algorithm design as a key factor in training stability.