GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient
  Fine-Tuning
Authors: name: Yeonjoon Jung, name: Daehyun Ahn, name: Hyungjun Kim, name: Taesu Kim, name: Eunhyeok Park

Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient
fine-tuning (PEFT) of generative models, valued for its simplicity and
effectiveness. Despite recent enhancements, LoRA still suffers from a
fundamental limitation: overfitting when the bottleneck is widened. It performs
best at ranks 32-64, yet its accuracy stagnates or declines at higher ranks,
still falling short of full fine-tuning (FFT) performance. We identify the root
cause as LoRA's structural bottleneck, which introduces gradient entanglement
to the unrelated input channels and distorts gradient propagation. To address
this, we introduce a novel structure, Granular Low-Rank Adaptation (GraLoRA)
that partitions weight matrices into sub-blocks, each with its own low-rank
adapter. With negligible computational or storage cost, GraLoRA overcomes
LoRA's limitations, effectively increases the representational capacity, and
more closely approximates FFT behavior. Experiments on code generation and
commonsense reasoning benchmarks show that GraLoRA consistently outperforms
LoRA and other baselines, achieving up to +8.5% absolute gain in Pass@1 on
HumanEval+. These improvements hold across model sizes and rank settings,
making GraLoRA a scalable and robust solution for PEFT. Code, data, and scripts
are available at https://github.com/SqueezeBits/GraLoRA.git