A Survey on LLM Mid-Training

Recent research, as highlighted in a survey published on arXiv, underscores the benefits of mid-training in foundation models, particularly in enhancing capabilities such as mathematics, coding, and reasoning. This intermediate training phase serves as a crucial bridge between the initial pre-training and subsequent post-training stages, effectively leveraging intermediate data and resources. By incorporating mid-training, models can improve their performance on complex tasks that require advanced reasoning skills. The findings align with ongoing discussions in the AI research community about optimizing training workflows to maximize model capabilities. This approach suggests a structured progression in model development, where mid-training plays a pivotal role in refining and expanding foundational skills. The survey contributes to a growing body of literature emphasizing the strategic importance of this training phase in the lifecycle of large language models.