The field of imperfect-information games and zero-shot reinforcement learning is rapidly advancing, with a focus on developing more efficient and effective algorithms for handling complex decision-making scenarios. Recent research has introduced new frameworks and techniques for improving the performance of imperfect-information games, such as the use of signal observation ordered games and full-recall outcome isomorphism. Additionally, there have been significant developments in zero-shot reinforcement learning, including the proposal of new methods such as Behavior-REgularizEd Zero-shot RL with Expressivity enhancement and Optimistic Task Inference for Behavior Foundation Models. These advances have the potential to enable more general and adaptable agents that can learn from limited data and adapt to new tasks quickly. Noteworthy papers in this area include: Beyond Outcome-Based Imperfect-Recall, which introduces a new framework for hand abstraction in imperfect-information games. Towards Robust Zero-Shot Reinforcement Learning, which proposes a new method for enhancing the expressivity and stability of zero-shot reinforcement learning. A Unified Framework for Zero-Shot Reinforcement Learning, which provides a comprehensive framework for understanding and comparing different approaches to zero-shot reinforcement learning.