Fast Adaptation Pipeline for LLMs Through Structured Gradient Approximation

Abstract

This paper focuses on the problem of efficient adaptation of large language models to downstream tasks. It proposes a fast fine-tuning strategy based on gradient approximation to address challenges such as high resource consumption and large training costs during the fine-tuning phase. The method keeps the backbone model parameters frozen. It introduces a gradient approximation module to model the optimization direction. Combined with a lightweight parameter update mechanism, it enables rapid convergence and performance transfer for specific tasks. The model framework consists of three core components: input encoding, approximate gradient construction, and lightweight parameter update. The approximation module builds on semantic representations to predict update directions related to the target loss. These directions then guide the fine-tuning process. To systematically evaluate the performance of the proposed method, the study designs multiple experiments. These include tests on hyperparameter sensitivity, data perturbation effects, and changes in structural depth. Representative datasets are selected, and the method is compared against various mainstream fine-tuning approaches. Experimental results show that the proposed method significantly reduces the proportion of trainable parameters while maintaining high task performance. It achieves a good balance among model accuracy, convergence speed, and resource efficiency. The paper also further analyzes the method's stability under challenging scenarios such as distribution shift and reduced sample size. These results validate the effectiveness of the structural design and the adaptability of the proposed approach.