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[2602.22681] Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement
Summary
This paper introduces LITE, a new strategy for accelerating the pre-training of large language models (LLMs) by optimizing training dynamics along flat directions in the optimization landscape.
Why It Matters
As large language models require significant computational resources for pre-training, improving optimizer efficiency is crucial. This research addresses the anisotropic nature of the optimization landscape, offering a method that enhances convergence speed, which can lead to more efficient model training and deployment.
Key Takeaways
- LITE enhances training dynamics by applying larger Hessian damping coefficients along flat trajectories.
- The proposed method significantly accelerates existing optimizers like Muon and SOAP across various architectures and datasets.
- Theoretical analysis supports faster convergence in anisotropic landscapes, improving pre-training efficiency.
Computer Science > Machine Learning arXiv:2602.22681 (cs) [Submitted on 26 Feb 2026] Title:Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement Authors:Shuchen Zhu, Rizhen Hu, Mingze Wang, Mou Sun, Xue Wang, Kun Yuan, Zaiwen Wen View a PDF of the paper titled Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement, by Shuchen Zhu and 6 other authors View PDF HTML (experimental) Abstract:Pre-training Large Language Models requires immense computational resources, making optimizer efficiency essential. The optimization landscape is highly anisotropic, with loss reduction driven predominantly by progress along flat directions. While matrix-based optimizers such as Muon and SOAP leverage fine-grained curvature information to outperform AdamW, their updates tend toward isotropy -- relatively conservative along flat directions yet potentially aggressive along sharp ones. To address this limitation, we first establish a unified Riemannian Ordinary Differential Equation (ODE) framework that elucidates how common adaptive algorithms operate synergistically: the preconditioner induces a Riemannian geometry that mitigates ill-conditioning, while momentum serves as a Riemannian damping term that promotes convergence. Guided by these insights, we propose LITE, a generalized acceleration strategy that enhances training dynamics by applying larger Hessian damping coefficients and learning rates along flat trajectories. Extensive experiments demonstr...
