RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation (2501.04315v2)

Abstract: Fine-tuning helps LLMs (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.

• The paper introduces RoRA, replacing LoRA's scaling factor α/r with α/√r to improve stability and performance in LLM fine-tuning.
• The methodology is validated on both uncompressed and pruned models, achieving an average accuracy boost of up to 6.5% over existing approaches.
• RoRA’s enhancements enable more cost-effective fine-tuning, offering practical benefits for deploying LLMs in specialized domains.

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

The paper presents a novel approach named RoRA (Rank-adaptive Reliability Optimization) to enhance the fine-tuning process of LLMs through an improved low-rank adaptation methodology. This research specifically addresses an observed deficiency in Low-Rank Adaptation (LoRA), which is widely employed due to its proficiency in maintaining model performance while reducing computational costs. However, LoRA's performance degradation with increasing rank size limits its applicability. The authors propose an optimized approach in RoRA to overcome this limitation, ensuring consistent performance improvements with increasing rank.

Key Contributions

1. Optimization of Scaling Factor: The fundamental improvement of RoRA lies in replacing LoRA's scaling factor $\alpha/r$ with $\alpha/\sqrt{r}$. This change ensures that, as the rank size grows, the model's performance is enhanced rather than degraded. The mathematical analysis provided highlights how this modification impacts the variance of gradient updates, promoting stability and improved convergence during the optimization process.
2. Application to Uncompressed and Pruned Models: RoRA has been validated across both uncompressed and pruned models, outperforming existing state-of-the-art techniques. This is particularly noteworthy in pruned models, where information recovery is more challenging due to reduced model complexity.
3. Performance Results: The empirical results demonstrate that RoRA significantly surpasses LoRA and the improved DoRA model in both average accuracy and task robustness. Specifically, for LLaMA-7B, RoRA achieves an average accuracy increase of 6.5% compared to LoRA. For extensively pruned models such as SHEARED-LLAMA-1.3B, RoRA maintains an advantage of 5.7% and 3.9% over LoRA and DoRA, respectively, underlining its efficacy in a resource-constrained environment.

Practical Implications and Future Directions

RoRA's effectiveness in managing large-scale LLM fine-tuning has substantial practical implications. By improving the efficiency of parameter updates, RoRA enables more cost-effective deployment of LLMs in specific application domains, like healthcare or law, where domain-specific language nuances are critical.

The proposed improvements potentially extend beyond LLMs, offering broader applicability in fine-tuning other types of deep learning models where similar low-rank adaptation strategies are used. Future work may explore the integration of RoRA with other parameter-efficient tuning strategies and apply it to different model architectures and tasks. Additionally, further exploration into the theoretical aspects of scaling factor optimization could yield deeper insights into adaptive tuning mechanisms for neural networks.

Concluding Remarks

RoRA introduces a significant enhancement to the field of model fine-tuning by addressing the critical issue of performance degradation with increased rank size in low-rank adaptations. This paper provides a comprehensive evaluation of RoRA, illustrating its practical benefits and solid theoretical foundation. As LLMs continue to grow in complexity and size, methods like RoRA become increasingly vital in optimizing computational resources while maintaining or enhancing model performance.