Revolutionizing Large Language Model Training through Dynamic Parameter Adjustment

• URL: http://arxiv.org/abs/2406.06564v1
• Date: Mon, 3 Jun 2024 05:40:34 GMT
• Title: Revolutionizing Large Language Model Training through Dynamic Parameter Adjustment
• Authors: Kaiye Zhou, Shucheng Wang,
• Abstract summary: We introduce a novel parameter-efficient training technique that frequently alters trainable part of parameters, facilitating effective pre-training. Our method achieves memory reductions and computational overhead comparable to current state-of-the-art parameter-efficient algorithms during the pre-training phase.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of large language models, the demand for efficient use of computational resources has become critically important. Although parameter-efficient fine-tuning techniques have achieved results comparable to full fine-tuning, their application during the pre-training phase poses significant challenges. Specifically, employing parameter-efficient strategies at the onset of pre-training can severely compromise efficiency, especially in larger models. In this paper, building upon the fine-tuning method LoRA, we introduce a novel parameter-efficient training technique that frequently alters trainable part of parameters, facilitating effective pre-training. Our method not only achieves memory reductions and computational overhead comparable to current state-of-the-art parameter-efficient algorithms during the pre-training phase but also maintains accuracy levels comparable to those of full pre-training. We provide both theoretical analyses and empirical evidence to demonstrate the effectiveness of our approach.

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