The field of large language models is moving towards addressing the limitations of current models in handling long-context tasks and dependencies. Researchers are exploring new benchmarking methods, data augmentation strategies, and training techniques to improve the performance of large language models in real-world applications. A key area of focus is the development of more efficient and effective methods for training and fine-tuning these models, including the use of intermediate data and computational resources. Noteworthy papers include: LooGLE v2, which introduces a novel benchmark for evaluating large language models' long context ability, and Tagging-Augmented Generation, which proposes a lightweight data augmentation strategy to boost large language model performance in long-context scenarios. ENTP is also a notable work, which enhances low-quality supervised fine-tuning data via neural-symbolic text purge-mix. Additionally, LongFilter and surveys on LLM mid-training and efficient large language model training provide valuable insights into the current state of the field and future research directions.