aiqbench analysis
This arXiv paper proposes LITE, an optimizer acceleration strategy for LLM pre-training that builds on a continuous-time Riemannian ODE view of adaptive optimizers like Muon and SOAP. Its claimed gains, reported across dense and MoE models from 130M to 1.3B parameters on C4 and Pile, are measured speedups in convergence rather than new capabilities, so readers should weigh them against the scale of the experiments, which stops well short of frontier model sizes. For ML practitioners, the more durable contribution may be the framing of momentum as Riemannian damping and preconditioners as geometry-inducing, which could guide future optimizer design.
Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement
arXiv:2602.22681v2 Announce Type: replace 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 use fine-grained curvature information to outperform AdamW, their updates tend toward isotropy, which is relatively conservative along flat directions yet potentially aggressive along sharp ones. To address this limitation, we first develop a unified Riemannian Ordinary Differential Equation (ODE) framework that offers a continuous-time interpretation of adaptive algorithms: 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 demonstrate that LITE significantly accelerates both Muon and SOAP across diverse architectures (Dense, MoE), parameter scales (130M to 1.3B), datasets (C4, Pile), and learning-rate schedules (cosine, wsd). Theoretical analysis confirms that LITE facilitates faster convergence along flat directions in anisotropic landscapes, providing a principled approach to efficient LLM pre-training. The code is available at https://github.com/SHUCHENZHU/LITE.
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 use fine-grained curvature information to outperform AdamW, their updates tend toward isotropy, which is relatively conservative along flat directions yet potentially aggressive along sharp ones. To address this limitation, we first develop a unified Riemannian Ordinary Differential Equation (ODE) framework that offers a continuous-time interpretation of adaptive algorithms: 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 demonstrate that LITE significantly accelerates both Muon and SOAP across diverse architectures (Dense, MoE), parameter scales (130M to 1.3B), datasets (C4, Pile), and learning-rate schedules (cosine, wsd). Theoretical analysis confirms that LITE facilitates faster convergence along flat directions in anisotropic landscapes, providing a principled approach to efficient LLM pre-training. The code is available at this https URL.
Machine Learning (cs.LG)
arXiv:2602.22681 [cs.LG]
(or arXiv:2602.22681v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.22681 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 282 words