The field of large language models (LLMs) is witnessing significant developments in code generation and reasoning capabilities. Researchers are exploring innovative approaches to improve the accuracy and logical correctness of generated code, particularly for underrepresented languages. The integration of explicit reasoning steps and reinforcement learning is showing promising results. Additionally, the impact of LLMs on code style and programming practices is being investigated, revealing measurable trends in the evolution of coding style. Noteworthy papers in this area include: From Reasoning to Code: GRPO Optimization for Underrepresented Languages, which introduces a generalizable approach for effective code generation in languages with limited public training data. code_transformed: The Influence of Large Language Models on Code, which provides large-scale empirical evidence of the effect of LLMs on real-world programming style. How Does LLM Reasoning Work for Code, which presents a comprehensive survey and taxonomy of code reasoning techniques and identifies gaps for future research.