The field of large language models (LLMs) is rapidly evolving, with a focus on improving pretraining and optimization techniques. Recent developments have centered around enhancing data quality, prompt optimization, and model adaptation. Researchers are exploring innovative methods to refine training data, such as preprocessing pipelines and data selection frameworks, to boost model performance. Moreover, prompt optimization has become a crucial aspect, with studies investigating state-space search problems, query-dependent prompt optimization, and evaluation-instructed frameworks. The use of metadata, such as URLs and document quality indicators, has also been found to accelerate pretraining. Furthermore, researchers are working on optimizing model parameters, including learning rate decay and anchor-based prompt learning. Noteworthy papers in this area include Blu-WERP, which presents a novel data preprocessing pipeline, and AnchorOPT, which introduces a dynamic anchor-based prompt learning framework. Additionally, the Majority of the Bests paper proposes a new selection mechanism that estimates the output distribution of Best-of-N via bootstrapping, while the A Unified Evaluation-Instructed Framework for Query-Dependent Prompt Optimization paper establishes a performance-oriented prompt evaluation framework. These advancements have significant implications for the development of more efficient and effective LLMs.