StructTune: Structured Sparse Fine-Tuning for Efficient Adaptation of Large Language Models

Abstract

Parameter-efficient fine-tuning (PEFT) methods like LoRA have become crucial for adapting large language models under computational constraints. While LoRA focuses on dense weight updates, recent sparse methods propose updating only selected portions of pretrained weights. However, these sparse approaches predominantly use element-wise parameter selection, overlooking inherent structured patterns in LLM weight distributions. We introduce StructTune1, a PEFT method that uses entire rows or columns as basic units for identifying task-relevant sparse components. Our analysis reveals LLaMA models exhibit distinct structured patterns in pretrained weights, with fine-tuning updates showing structured amplification rather than uniform changes. We develop a sensitivity-based framework to efficiently identify task-relevant weight structures without full fine-tuning. Experiments on mathematical reasoning and code generation demonstrate StructTune’s structured sparse updates outperform both dense LoRA and element-wise sparse methods while maintaining computational efficiency. Ablation studies validate our sensitivity-based selection strategy, confirming the efficacy of exploiting structured weight patterns in PEFT.