Omni-Masked Gradient Descent: Memory-Efficient Optimization via Mask Traversal with Improved Convergence

• URL: http://arxiv.org/abs/2603.05960v2
• Date: Tue, 10 Mar 2026 11:15:43 GMT
• Title: Omni-Masked Gradient Descent: Memory-Efficient Optimization via Mask Traversal with Improved Convergence
• Abstract summary: Memory-efficient optimization methods have recently gained increasing attention for scaling full-tuning of of large language models under the GPU-memory bottleneck.<n>We propose Omni-O-Masked Descent (OMGD) as an approach to memory-efficient training.
• Score: 5.683382109687174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memory-efficient optimization methods have recently gained increasing attention for scaling full-parameter training of large language models under the GPU-memory bottleneck. Existing approaches either lack clear convergence guarantees, or only achieve the standard ${\mathcal{O}}(ε^{-4})$ iteration complexity in the nonconvex settings. We propose Omni-Masked Gradient Descent (OMGD), an optimization method based on mask traversal for memory efficient training, and provide a nonconvex convergence analysis that establishes a strictly improved iteration complexity of $\tilde{\mathcal{O}}(ε^{-3})$ for finding an $ε$-approximate stationary point. Empirically, OMGD is a lightweight, plug-and-play approach that integrates seamlessly into most mainstream optimizers, yielding consistent improvements over competitive baselines in both fine-tuning and pre-training tasks.

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