The field of communication and reinforcement learning is witnessing significant developments, with a focus on efficient communication, adaptive learning, and robust decision-making. Researchers are exploring novel approaches to improve the performance of reinforcement learning agents in complex environments, such as formulating in-context reinforcement learning as a two-agent emergent communication problem. Additionally, there is a growing interest in goal-oriented communication, which prioritizes the importance of information with respect to the agents' shared objectives. This paradigm shift has the potential to revolutionize the way agents communicate and make decisions in multi-agent systems. Noteworthy papers in this area include: In-Context Reinforcement Learning via Communicative World Models, which introduces a framework that learns a transferable communicative context by decoupling latent representation learning from control. Toward Goal-Oriented Communication in Multi-Agent Systems: An overview, which provides a comprehensive survey of goal-oriented communication in multi-agent systems. Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks, which presents a novel framework for proactive resource management in vehicular networks using large language models.