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This paper introduces AdamPM, a memory-efficient variant of the Adam optimizer that reduces second-moment storage requirements by pooling adjacent entries without sacrificing training stability or speed. By grouping moment estimates along row or column dimensions, the method enables more efficient pre-training and fine-tuning of large language models while maintaining compatibility with existing hyperparameter settings. The approach offers a practical solution to the substantial memory overhead inherent in standard Adam implementations for LLM development.
AdamPM: Memory-Efficient LLM Optimization via Moment Pooling
arXiv:2610.24598v1 Announce Type: new Abstract: Adam is widely used for training large language models (LLMs) owing to its strong optimization performance and training stability, but its element-wise estimation for the first and second moments incur substantial memory overhead. Although numerous memory-efficient optimizers have been proposed, many incur additional computational overhead or require additional tuning of hyperparameters. Consequently, Adam remains a dominant and reliable choice for LLM pre-training. We propose AdamPM (Adam with Pooled Moments), a simple and memory-efficient variant of Adam that groups adjacent moment entries along the row or column dimension and represents each group by a single shared mean. AdamPM can well inherit Adam's hyperparameter settings and is readily integrated into existing Adam optimizer implementations, with less memory overhead. We systematically investigate pooling of both first and second moment estimates and identify second moment pooling as the effective design. With a group size of $s$, AdamPM reduces second moment memory to $1/s$ of the original requirement. Extensive experiments on language model pretraining, fine-tuning and multimodal learning demonstrate that AdamPM maintains model performance and training speed while substantially reducing optimizer-state memory. Our code is available at \url{https://github.com/jixiaowei95/AdamPM}.
Abstract:Adam is widely used for training large language models (LLMs) owing to its strong optimization performance and training stability, but its element-wise estimation for the first and second moments incur substantial memory overhead. Although numerous memory-efficient optimizers have been proposed, many incur additional computational overhead or require additional tuning of hyperparameters. Consequently, Adam remains a dominant and reliable choice for LLM pre-training. We propose AdamPM (Adam with Pooled Moments), a simple and memory-efficient variant of Adam that groups adjacent moment entries along the row or column dimension and represents each group by a single shared mean. AdamPM can well inherit Adam's hyperparameter settings and is readily integrated into existing Adam optimizer implementations, with less memory overhead. We systematically investigate pooling of both first and second moment estimates and identify second moment pooling as the effective design. With a group size of $s$, AdamPM reduces second moment memory to $1/s$ of the original requirement. Extensive experiments on language model pretraining, fine-tuning and multimodal learning demonstrate that AdamPM maintains model performance and training speed while substantially reducing optimizer-state memory. Our code is available at \url{this https URL}.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.24598 [cs.LG]
(or arXiv:2610.24598v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.24598 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 280 words