ToPA method improves efficient fine-tuning of large AI models
ToPA: Block-wise Toeplitz Adaptation for Expressive and Efficient Fine-Tuning
Artificial IntelligenceMachine Learning
Summary
Fine-tuning very large AI models to perform new tasks usually requires many adjustments, which can be expensive and slow. The authors noticed that existing efficient methods restrict how much the model can change, leading to weaker results. They introduced a technique called ToPA that uses special matrix structures to allow more flexible fine-tuning without needing many parameters. Their method stabilizes training and works better across various language and vision tasks compared to earlier approaches.
What this means in practice
- •For machine learning engineers: Efficiently adapt large pre-trained models to new NLP or computer vision tasks with improved accuracy and reduced computation using ToPA.
- •For cloud ai service providers: Deploy ToPA to enable faster and more resource-friendly fine-tuning of customer models, improving service scalability and responsiveness.$Commercial implications: ToPA enables more compact and stable fine-tuning, allowing AI services to offer flexible model updates at lower computational cost.
Authors
Sicong Li, Qianqian Xu, Zhiyong Yang, Zitai Wang, Longtao Huang, Xiaochun Cao, Qingming Huang
Abstract
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a prevalent strategy for adapting large pre-trained models. Among PEFT techniques, reparameterization-based methods, particularly those that employ low-rank or sparsity structures, have attracted significant attention. However, these approaches often underperform full fine-tuning. Specifically, low-rank methods constrain the update rank, which is misaligned with the inherently high-rank nature of full fine-tuning. In contrast, sparsity-based techniques limit the flexibility of the parameter space and compromise connectivity across weights. To overcome these limitations, we explore the use of Toeplitz matrices, whose entries are constant along each diagonal, therefore providing a compact parameterization without imposing explicit rank or sparsity constraints. A straightforward approach is to update weights via a product of Toeplitz matrices. However, similar to RNNs, long multiplicative chains often lead to gradient instability. To address this issue, we propose \textbf{ToPA}, a more refined version of this idea that replaces standard Toeplitz matrices with block-wise Toeplitz, greatly increasing each factor's capacity and shortening the chain, thereby stabilizing training and improving expressiveness. We theoretically show that products of block-wise Toeplitz matrices can approximate arbitrary matrices under shorter chain length, justifying the structural design of ToPA. Furthermore, we demonstrate that ToPA offers greater expressivity than existing low-rank and sparse parameterizations. Empirical evaluations across 21 NLP and CV datasets, spanning 5 model architectures, consistently validate the effectiveness of ToPA.