The field of reinforcement learning and online optimization is moving towards addressing fundamental challenges such as replicability, efficiency, and adaptability. Researchers are exploring innovative approaches to ensure stable and reliable performance in complex environments. Noteworthy papers in this area include: List Replicable Reinforcement Learning, which introduces a novel planning strategy to achieve list replicability, and Efficient Matroid Bandit Linear Optimization Leveraging Unimodality, which exploits unimodal structure to improve time complexity. Additionally, papers like Retrieval-Augmented Memory for Online Learning and Improved Training Mechanism for Reinforcement Learning via Online Model Selection are making significant contributions to online learning and model selection. These advances have the potential to impact various applications, from energy-efficient lighting control to heterogeneous treatment effect estimation in large-scale industrial settings.