做模型微调的开发者如果受限于LoRA的低秩瓶颈，SMoA提供了一种在更少参数下覆盖更广频谱方向的新思路，值得在资源敏感任务中尝试。
SMoA（频谱调制适配器）是一种新的参数高效微调方法，旨在解决LoRA在低秩设置下表达能力受限的问题。理论表明，LoRA微调仅能捕获预训练权重矩阵的前r个奇异值，而增大秩虽能提升性能但会增加计算成本。SMoA通过将层划分为多个对齐的频谱块，并在每个对角块内应用Hadamard调制的低秩分支，从而在更小的参数预算下覆盖更广的预训练频谱方向。实验结果显示，在低预算设置下，SMoA在多项任务上的平均性能优于LoRA及其他同类基线方法。该方法为资源受限场景下的模型微调提供了更高效的解决方案。
SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation (LoRA) uses a low-rank update method to simulate full parameter fine-tuning, which is widely used to reduce resource requirements. However, decreasing the rank encounters challenges with limited representational capacity. Theory suggests that LoRA fine-tuning with rank r converges toward the top r singular values of the pre-trained weight matrix. As the rank increases, more principal singular directions are preserved, which generally improves the model's performance. However, a larger rank also introduces more trainable parameters, leading to higher computational cost. To overcome this dilemma, we propose SMoA, a \textbf{S}pectrum \textbf{Mo}dulation \textbf{A}dapter that enlarges the accessible family of spectrum-aware updates under a smaller parameter budget. SMoA partitions the layer into multiple aligned spectral blocks and applies one in-block Hadamard-modulated low-rank branch to each diagonal block, yielding broader coverage of pretrained spectral directions. We provide theoretical analysis and empirical results on multiple tasks. In our experiments, SMoA improves average performance in the current lower-budget setting over LoRA and competitive LoRA-style baselines.