MLorc: Momentum Low-rank Compression for Large Language Model Adaptation

• URL: http://arxiv.org/abs/2506.01897v2
• Date: Tue, 03 Jun 2025 03:59:44 GMT
• Title: MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
• Authors: Wei Shen, Zhang Yaxiang, Minhui Huang, Mengfan Xu, Jiawei Zhang, Cong Shen,
• Abstract summary: We propose a memory-efficient training paradigm called Momentum Low-rank compression (MLorc)<n>By directly compressing and reconstructing momentum rather than gradients, MLorc avoids imposing a fixed-rank constraint on weight update matrices.<n> Empirically, MLorc consistently outperforms other memory-efficient training methods, matches or even exceeds the performance of full fine-tuning.
• Score: 18.63642841688227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With increasing size of large language models (LLMs), full-parameter fine-tuning imposes substantial memory demands. To alleviate this, we propose a novel memory-efficient training paradigm called Momentum Low-rank compression (MLorc). By directly compressing and reconstructing momentum rather than gradients, MLorc avoids imposing a fixed-rank constraint on weight update matrices and better preserves the training dynamics of full-parameter fine-tuning, in contrast to existing low-rank approaches such as LoRA and GaLore. Empirically, MLorc consistently outperforms other memory-efficient training methods, matches or even exceeds the performance of full fine-tuning with a small rank (e.g., $r=4$), and generalizes well across different optimizers -- all while not compromising time or memory efficiency. Furthermore, we provide a theoretical guarantee for its convergence under reasonable assumptions.

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