Two-Stage Parameter Alignment for Multi-LoRA Merging in Large Language Models

Merging a large number of low-rank adaptations (LoRAs) is a key technology for enhancing the integration and deployment efficiency of large language models (LLMs). However, current general model merging methods are prone to "parameter interference" problem, and this issue is especially pronounced when merging high-rank LoRAs, where parameter conflicts tend to be more severe. While the classical rotation alignment approach can enhance robustness, it is difficult to apply due to incompatibility with the LoRA structure and its high computational complexity. To address these challenges, we propose a novel two-stage parameter alignment (TSPA) framework. TSPA is designed from the perspective of the LoRA architecture, overcoming the limitations of existing methods and reducing the computational complexity from quadratic to linear. We conduct experiments on Natural Language Processing (NLP) tasks using models such as Llama-3-8B. The results show that the two-stage design of TSPA achieves a balance between task capabilities and general knowledge. It exhibits greater robustness than other methods in highrank and high-interference scenarios, while effectively preserving fine-grained functions.