In this paper reading, LIMA (Less Is More for Alignment) demonstrates the efficiency and effectiveness of large language models through pre-training and minimal fine-tuning, outperforming its contemporaries in various evaluations, including human preference and GPT-4 comparisons. The research highlights the power of pre-training and the importance of data quality, diversifying the training data beyond just questions and online community sets to achieve better results. The findings suggest that input diversity and output quality have a significant impact on the performance of large language models, and that fine-tuning can be more effective than prompt engineering in certain cases. The paper also discusses the limitations of current methods and the need for further research on fine-tuning and alignment.