New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework
Summary: arXiv:2604.09940v1 Announce Type: new
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.
Introduction
The field of natural language processing has witnessed significant advancements due to the development of Large Language Models (LLMs). However, the challenge of effectively fine-tuning these models remains a critical concern for researchers and practitioners alike. Traditional methods, while effective, often come with trade-offs that can hinder performance and efficiency. Our research introduces a new hybrid fine-tuning paradigm that aims to bridge the gap between full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT).
Limitations of Current Approaches
Both full fine-tuning and PEFT approaches face distinct challenges:
- Full Fine-Tuning: This method updates all model parameters, which can be computationally intensive and require significant resources, making it less accessible for smaller organizations.
- Parameter-Efficient Fine-Tuning (PEFT): While it conserves computational resources by only adjusting a subset of parameters, PEFT often struggles with knowledge retention and can exhibit less-than-optimal performance on new tasks.
The Proposed Hybrid Approach
Our hybrid fine-tuning method addresses the limitations of both techniques. By jointly updating LLMs and PEFT modules, we utilize a combination of zeroth-order and first-order optimization methods. This innovative approach allows for:
- Improved efficiency in model fine-tuning without sacrificing performance.
- Enhanced capability to learn new knowledge while retaining previously acquired information.
- A balanced optimization process that adapts to the complexities of the training landscape.
Theoretical Framework and Convergence Analysis
To rigorously analyze our hybrid algorithm, we developed a theoretical framework based on the concept of hybrid smoothness condition. This framework effectively accounts for the heterogeneous nature of the optimization landscape in joint LLM and PEFT training. Our convergence analysis utilizes reshuffling-type Stochastic Gradient Descent (SGD) under multiple learning rates, guaranteeing robust performance across various scenarios.
Empirical Validation
Through extensive empirical studies across diverse downstream tasks and model architectures, our results demonstrate a consistent performance improvement compared to traditional fine-tuning methods. This validation confirms the efficacy of our hybrid fine-tuning approach as a viable solution for large-scale language model fine-tuning.
Conclusion
In summary, our proposed hybrid fine-tuning paradigm for LLMs represents a significant advancement in model optimization techniques. By addressing the inherent limitations of existing methods, we pave the way for more efficient and effective fine-tuning strategies that can benefit a wide range of applications in the natural language processing domain.