The field of large language models (LLMs) and optimization techniques is rapidly evolving, with a focus on developing more efficient, scalable, and interpretable methods. Recent research has explored the application of LLMs to specific domains, such as tourism and e-commerce, and has introduced novel frameworks for evaluating and optimizing their performance. The use of chain-of-thought reasoning, expert-guided optimization, and reinforcement learning has shown promising results in improving the accuracy and effectiveness of LLMs. Additionally, advancements in synthetic data generation and management have enabled the creation of high-quality datasets for training and fine-tuning LLMs. Noteworthy papers include: LETToT, which proposes a label-free evaluation framework for LLMs in tourism, and TaoSR1, which introduces a novel paradigm for applying chain-of-thought reasoning to relevance classification in e-commerce. OS-R1 is also notable for its agentic Linux kernel tuning framework powered by rule-based reinforcement learning. Overall, these developments are advancing the field of LLMs and optimization techniques, enabling more efficient and effective solutions for complex problems.