LDAdam is a novel optimization approach that significantly reduces memory requirements when training large language models by performing adaptive optimization in lower-dimensional subspaces.

• Memory footprint reduction to a fraction of model size while maintaining performance
• Projection-aware update rules that enable smooth transitions between subspaces
• Full parameter space exploration throughout the training process
• Practical solution for resource-constrained training environments

This research directly addresses one of the key engineering challenges in modern AI: enabling effective training of increasingly large models with limited computational resources. For organizations deploying their own models, LDAdam offers a practical path to more efficient training pipelines.

LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics