Papers › R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning

R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning

21 Feb 2025arXiv:2502.15455archive 2025-07-28

Jinda Liu, Yi Chang, Yuan Wu

Fine-tuning large language models (LLMs) is prohibitively expensive in terms of computational and memory costs. Low-rank Adaptation (LoRA), as one of the most popular parameter-efficient fine-tuning (PEFT) methods, offers a cost-effective alternative by approximating the model changes ΔW ∈ℝ^(m ×n) through the product of down-projection matrix A ∈ℝ^(m ×r) and head matrix B ∈ℝ^(r ×n), where r ≪min(m, n). 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 Random Initialization and Multi-Head Dropout, enabling more efficient learning of task-specific features while maintaining shared knowledge representation. Extensive experiments demonstrate that R-LoRA is better at capturing task-specific knowledge, thereby improving performance in multi-task scenarios. The code is available at https://github.com/jinda-liu/R-LoRA.

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