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 high-rank and high-interference scenarios, while effectively preserving fine-grained functions.

Knowledge distillation effectively transfers reasoning capabilities from large language models to lightweight student models. To enable knowledge transfer across disparate model families, researchers increasingly explore cross-tokenizer distillation. However, cross-tokenizer distillation remains challenging due to vocabulary and sequence misalignment, while approximate vocabulary alignment can introduce additional noise into distillation. To address these challenges, we propose Anchor-Based Cross-Tokenizer Distillation with Residual Regularization (ACTD). ACTD bridges structural heterogeneity through vocabulary and sequence alignment, while mitigating alignment noise via a novel anchor loss with residual regularization. We further extend this framework to a multi-teacher setting. Evaluated across five reasoning benchmarks with three distinct teacher models, ACTD achieves state-of-the-art performance. Moreover, its multi-teacher extension outperforms the strongest single-teacher and multi-teacher baselines, further demonstrating the robustness of our method.

With the development of large language models, fine-tuning has emerged as an effective method to enhance performance in specific scenarios by injecting domain-specific knowledge. In this context, model merging techniques provide a solution for fusing knowledge from multiple fine-tuning models by combining their parameters. However, traditional methods often encounter task interference when merging full fine-tuning models, and this problem becomes even more evident in parameter-efficient fine-tuning scenarios. In this paper, we introduce an improvement to the RegMean method, which indirectly leverages the training data to approximate the outputs of the linear layers before and after merging. We propose an adaptive merging method called FroM, which directly measures the model parameters using the Frobenius norm, without any training data. By introducing an additional hyperparameter for control, FroM outperforms baseline methods across various fine-tuning scenarios, alleviating the task interference problem.