PrunedLoRA: Robust Gradient-Based structured pruning for Low-rank Adaptation in Fine-tuning

• URL: http://arxiv.org/abs/2510.00192v2
• Date: Sat, 01 Nov 2025 04:19:13 GMT
• Title: PrunedLoRA: Robust Gradient-Based structured pruning for Low-rank Adaptation in Fine-tuning
• Authors: Xin Yu, Cong Xie, Ziyu Zhao, Tiantian Fan, Lingzhou Xue, Zhi Zhang,
• Abstract summary: Low-rank adaptation (LoRA) has become a widely used paradigm for parameter-efficient fine-tuning of large language models.<n>We propose textitPrunedLoRA, a new framework that leverages structured pruning to obtain highly representative low-rank adapters.
• Score: 18.3077556191671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-rank adaptation (LoRA) has become a widely used paradigm for parameter-efficient fine-tuning of large language models, yet its representational capacity often lags behind full fine-tuning. Within the context of LoRA, a key open question is how to obtain expressive low-rank adapters from over-parameterized spaces. We propose \textit{PrunedLoRA}, a new framework that leverages structured pruning to obtain highly representative low-rank adapters from an over-parameterized initialization. Unlike prior approaches that impose a fixed low-rank budget, PrunedLoRA dynamically prunes less important components during fine-tuning and prevents their reactivation, enabling flexible and adaptive rank allocation. For structured pruning, by minimizing the pruning error for overall loss, we provide fine-grained pruning and recovery updates in a gradient-based pruning strategy with grounded interpretation. We provide the first theoretical analysis of the robustness of structured pruning and provably show that under the impact of weight perturbation, gradient-based pruning is more robust than activation-based pruning with respect to overall loss. Empirically, PrunedLoRA consistently outperforms LoRA and its variants across supervised fine-tuning tasks in mathematical reasoning, code generation, and natural language understanding, and it also demonstrates advantages over existing structured pruning methods across diverse sparsity levels.

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