发表机构
NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对高阶优化器在大规模语言模型预训练中的挑战，通过改进预处理梯度方法、统一实证研究不同优化器、引入分层分布式优化器及系统级改进，提升训练效果，最终发布含新兴优化算法的代码库。
AI 中文摘要
高阶优化器如Muon和SOAP比AdamW收敛更快，但计算成本和数值稳定性挑战限制了其大规模应用。本文调整并增强预处理梯度方法以克服大规模语言模型预训练的实际挑战。先识别SOAP在大批量时的不稳定性并提出算法改进，包括逐步QR正交化和改进预处理策略。然后对SOAP、Muon和AdamW进行统一实证研究，确保跨优化器公平转移学习率。实验表明SOAP和Muon在测试规模上始终优于AdamW。还引入与Megatron-LM兼容的分层分布式优化器并进行系统级改进，最后发布包含新兴优化算法的代码库。
英文摘要
Higher-order optimizers such as Muon and SOAP offer faster convergence than AdamW, but their computational cost and numerical stability challenges have limited adoption at scale. In this work, we adapt and enhance preconditioned gradient methods to overcome the practical challenges of large-scale LLM pretraining. We first identify instabilities in SOAP at large batch sizes and propose algorithmic modifications including per-step QR orthogonalization and improved preconditioning strategies that eliminate loss spikes and enable stable training in these regimes. We then present a unified empirical study of SOAP, Muon, and AdamW using update-RMS matching to ensure fair learning rate transfer across optimizers. As part of this analysis, we empirically evaluate the orthogonalization quality of Muon. Our experiments on multi-billion-parameter models trained on trillions of tokens reveal that SOAP and Muon consistently outperform AdamW at the scales we tested. Notably, at batch sizes of up to 100M tokens for next-token prediction, these optimizers maintain training stability and quality while AdamW degrades. To enable efficient training at large scale, we introduce a layer-wise distributed optimizer compatible with Megatron-LM. Our implementation balances memory and hides communication while avoiding approximations to the optimizer computations, thus retaining their convergence benefits. Additionally, we identify and build specific system-level improvements to further accelerate our layer-wise implementation. To support the research community, we release a codebase that contains emerging algorithms for optimization: https://github.com/NVIDIA-NeMo/Emerging-Optimizers
