RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation

• URL: http://arxiv.org/abs/2501.04315v2
• Date: Sat, 11 Jan 2025 18:17:46 GMT
• Title: RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation
• Authors: Jun Liu, Zhenglun Kong, Peiyan Dong, Changdi Yang, Xuan Shen, Pu Zhao, Hao Tang, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, Dong Huang, Yanzhi Wang,
• Abstract summary: Low-Rank Adaptation (LoRA) is widely used and effective for fine-tuning large language models. We propose RoRA (Rank-adaptive Reliability Optimization), a simple yet effective method for optimizing LoRA's scaling factor. RoRA ensures improved performance as rank size increases and excels in the more challenging task of accuracy recovery when fine-tuning pruned models.
• Score: 59.34193580856381
• License:
• Abstract: Fine-tuning helps large language models (LLM) recover degraded information and enhance task performance. Although Low-Rank Adaptation (LoRA) is widely used and effective for fine-tuning, we have observed that its scaling factor can limit or even reduce performance as the rank size increases. To address this issue, we propose RoRA (Rank-adaptive Reliability Optimization), a simple yet effective method for optimizing LoRA's scaling factor. By replacing $\alpha/r$ with $\alpha/\sqrt{r}$, RoRA ensures improved performance as rank size increases. Moreover, RoRA enhances low-rank adaptation in fine-tuning uncompressed models and excels in the more challenging task of accuracy recovery when fine-tuning pruned models. Extensive experiments demonstrate the effectiveness of RoRA in fine-tuning both uncompressed and pruned models. RoRA surpasses the state-of-the-art (SOTA) in average accuracy and robustness on LLaMA-7B/13B, LLaMA2-7B, and LLaMA3-8B, specifically outperforming LoRA and DoRA by 6.5% and 2.9% on LLaMA-7B, respectively. In pruned model fine-tuning, RoRA shows significant advantages; for SHEARED-LLAMA-1.3, a LLaMA-7B with 81.4% pruning, RoRA achieves 5.7% higher average accuracy than LoRA and 3.9% higher than DoRA.

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