Abstract: This paper presents a comprehensive study on meta-learning techniques for the efficient fine-tuning of large language models (LLMs). The research investigates the application of meta-learning strategies to enhance the adaptability and performance of LLMs with limited computational resources. The findings demonstrate significant improvements in fine-tuning efficiency and model performance, as evidenced by statistical analyses and experimental results.