The field of large language models (LLMs) is rapidly evolving, with a focus on improving fine-tuning methods to adapt these models to specific tasks and domains. Recent research has explored innovative approaches to fine-tuning, including the use of evolution strategies, prompt optimization, and layer-wise parameter-efficient fine-tuning. These advancements have led to significant improvements in the performance and efficiency of LLMs, enabling them to be applied to a wider range of tasks and domains. Notably, the development of new fine-tuning methods has also led to increased interest in the use of LLMs for tasks such as combinatorial optimization and control policy synthesis. Overall, the field is moving towards more efficient, effective, and scalable fine-tuning methods that can unlock the full potential of LLMs. Noteworthy papers include: Fine-tuning Done Right in Model Editing, which introduces a simple and effective localized editing method, and Evolution Strategies at Scale, which demonstrates the scalability of evolution strategies for fine-tuning LLMs. Additionally, Combining Large Language Models and Gradient-Free Optimization for Automatic Control Policy Synthesis presents a hybrid approach that decouples structural synthesis from parameter optimization, achieving higher returns and improved sample efficiency.