New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework

2026-04-10

AI Summary

This work addresses the trade-off between the high computational cost of full-parameter fine-tuning and the limited learning capacity of parameter-efficient fine-tuning (PEFT) methods for large language models. The authors propose a hybrid fine-tuning paradigm that jointly optimizes the backbone network and PEFT modules by integrating zeroth- and first-order optimization techniques. They introduce, for the first time, a mixed smoothness condition to characterize the heterogeneous optimization landscape inherent in such hybrid settings. Building upon this, they develop a theoretical convergence framework for a multi-learning-rate reshuffled stochastic gradient descent algorithm, enabling efficient co-adaptation of model components. Experimental results demonstrate that the proposed method consistently achieves significant performance gains across diverse downstream tasks and model architectures while maintaining computational efficiency, thereby validating its effectiveness and scalability.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Fine-tuning Large Language Models (LLMs) typically involves either full fine-tuning, which updates all model parameters, or Parameter-Efficient Fine-Tuning (PEFT), which adjusts a small subset of parameters. However, both approaches have inherent limitations: full fine-tuning is computationally expensive, while PEFT often struggles to learn new knowledge and exhibits suboptimal performance. To overcome these issues, we propose a novel hybrid fine-tuning approach that jointly updates both LLMs and PEFT modules using a combination of zeroth-order and first-order optimization methods. To analyze our new algorithm, we develop a theoretical framework centered on the concept of hybrid smoothness condition, which accounts for the heterogeneous nature of the optimization landscape in joint LLM and PEFT training. We derive a rigorous convergence analysis for the convergence of reshuffling-type SGD algorithm under multiple learning rates and demonstrate its effectiveness through extensive empirical studies across various downstream tasks and model architectures. On the practical side, our results demonstrate consistent performance improvement, making the approach a viable solution for large-scale language model fine-tuning.

Problem

• Large Language Models
• Fine-tuning
• Parameter-Efficient Fine-Tuning
• Computational Cost
• Model Performance

Innovation

• hybrid fine-tuning
• zeroth-order optimization
• first-order optimization
• convergence analysis
• parameter-efficient fine-tuning