Continual Gradient Low-Rank Projection Fine-Tuning for LLMs

• URL: http://arxiv.org/abs/2507.02503v1
• Date: Thu, 03 Jul 2025 10:11:22 GMT
• Title: Continual Gradient Low-Rank Projection Fine-Tuning for LLMs
• Authors: Chenxu Wang, Yilin Lyu, Zicheng Sun, Liping Jing,
• Abstract summary: Low-Rank Adaptation (LoRA) offers efficiency but constrains the model's ability to learn new tasks and transfer knowledge.<n>We propose GORP (Gradient LOw Rank Projection) for Continual Learning, a novel training strategy that overcomes these limitations.<n>Experiments on continual learning benchmarks demonstrate GORP's superior performance compared to existing state-of-the-art approaches.
• Score: 20.978406031958965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness. Low-Rank Adaptation (LoRA) offers efficiency but constrains the model's ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit parameter constraints. We propose GORP (Gradient LOw Rank Projection) for Continual Learning, a novel training strategy that overcomes these limitations by synergistically combining full and low-rank parameters and jointly updating within a unified low-rank gradient subspace. GORP expands the optimization space while preserving efficiency and mitigating catastrophic forgetting. Extensive experiments on continual learning benchmarks demonstrate GORP's superior performance compared to existing state-of-the-art approaches. Code is available at https://github.com/Wcxwcxw/GORP.

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