AdamPM: Memory-Efficient LLM Optimization via Moment Pooling
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 we...
| Authors | Xiaowei Ji, Lishan Xu, Jianing An et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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