DiaBlo: Diagonal Blocks Are Sufficient For Finetuning

TL;DR

DiaBlo introduces a parameter-efficient fine-tuning method that updates only diagonal blocks of model weights, achieving competitive performance with improved stability and simplicity over existing approaches.

Contribution

DiaBlo is a novel PEFT approach that updates only diagonal blocks, avoiding low-rank matrix products and enhancing convergence stability and expressiveness.

Findings

Achieves competitive accuracy across diverse tasks.

Maintains high memory efficiency and fast training speed.

Provides theoretical guarantees of improved expressiveness.

Abstract

Fine-tuning is a critical step for adapting large language models (LLMs) to domain-specific downstream tasks. To mitigate the substantial computational and memory costs of full-model fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed to update only a small subset of model parameters. However, performance gaps between PEFT approaches and full-model fine-tuning still exist. In this work, we present DiaBlo, a simple yet effective PEFT approach that updates only the diagonal blocks of selected model weight matrices. Unlike Low-Rank Adaptation (LoRA) and its variants, DiaBlo eliminates the need for low-rank matrix products, thereby avoiding the reliance on auxiliary initialization schemes or customized optimization strategies to improve convergence. This design leads to stable and robust convergence while maintaining comparable memory efficiency and training speed to LoRA. Moreover, we provide theoretical guarantees showing that, under mild low-rank conditions, DiaBlo is more expressive than LoRA in the linear problem and converges to a stationary point of the general nonlinear full fine-tuning. Through extensive experiments across a range of tasks, including commonsense reasoning, arithmetic reasoning, code generation, and safety alignment, we show that fine-tuning only diagonal blocks is sufficient for strong and consistent performance. DiaBlo not only achieves competitive accuracy but also preserves high memory efficiency and fast fine-tuning speed. Codes are available at https://github.com/ziyangjoy/DiaBlo.