The field of medical language models is rapidly evolving, with a focus on improving the comprehension and generation of medical texts. Recent developments have seen the introduction of innovative models and techniques that enhance the performance and interpretability of these models. One notable trend is the use of reinforcement learning and self-synthesis methods to generate high-quality instruction data, which has been shown to improve the accuracy and reliability of medical language models. Another area of research is the development of models that can effectively reason with code and generate precise computations, symbolic manipulations, and algorithmic reasoning. These advances have the potential to significantly improve the application of medical language models in clinical workflows and health information infrastructures. Noteworthy papers include Medalyze, which provides a practical and lightweight solution for improving information accessibility in healthcare, and LongMagpie, which introduces a self-synthesis framework for generating large-scale long-context instruction data. WiNGPT-3.0 and AlphaMed are also notable for their advancements in medical reasoning and interpretable decision-making. Infinite-Instruct is another significant contribution, providing a scalable solution for LLM training in programming.