R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913869303119872 |
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| author | Liu, Jinda Chang, Yi Wu, Yuan |
| author_facet | Liu, Jinda Chang, Yi Wu, Yuan |
| contents | Fine-tuning large language models (LLMs) is computationally expensive, and Low-Rank Adaptation (LoRA) provides a cost-effective solution by approximating weight updates through low-rank matrices. In real-world scenarios, LLMs are fine-tuned on data from multiple domains to perform tasks across various fields, embodying multi-task learning (MTL). LoRA often underperforms in such complex scenarios. To enhance LoRA's capability in multi-task learning, we propose R-LoRA, which incorporates Multi-Head Randomization. Multi-Head Randomization diversifies the head matrices through Multi-Head Dropout and Multi-Head Random Initialization, enabling more efficient learning of task-specific features while maintaining shared knowledge representation. Our approach not only improves performance in MTL but also reduces GPU memory usage and training time. Experiments show that R-LoRA's gains stem from increased diversity in the head matrices, demonstrating its effectiveness for multi-task learning. The code is available at https://github.com/jinda-liu/R-LoRA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning Liu, Jinda Chang, Yi Wu, Yuan Machine Learning Artificial Intelligence Fine-tuning large language models (LLMs) is computationally expensive, and Low-Rank Adaptation (LoRA) provides a cost-effective solution by approximating weight updates through low-rank matrices. In real-world scenarios, LLMs are fine-tuned on data from multiple domains to perform tasks across various fields, embodying multi-task learning (MTL). LoRA often underperforms in such complex scenarios. To enhance LoRA's capability in multi-task learning, we propose R-LoRA, which incorporates Multi-Head Randomization. Multi-Head Randomization diversifies the head matrices through Multi-Head Dropout and Multi-Head Random Initialization, enabling more efficient learning of task-specific features while maintaining shared knowledge representation. Our approach not only improves performance in MTL but also reduces GPU memory usage and training time. Experiments show that R-LoRA's gains stem from increased diversity in the head matrices, demonstrating its effectiveness for multi-task learning. The code is available at https://github.com/jinda-liu/R-LoRA |
| title | R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.15455 |