The field of reinforcement learning and multi-agent systems is rapidly advancing, with a focus on developing more efficient, scalable, and interpretable methods. Recent developments have seen a shift towards hierarchical reinforcement learning, dynamic planning, and the use of large language models to improve agent performance. Notably, researchers are exploring the use of language-driven hierarchical task structures as explicit world models for multi-agent learning, which has shown promise in improving sample efficiency and enabling more sophisticated strategic behaviors. Noteworthy papers include: Scalable Option Learning, which proposes a highly scalable hierarchical RL algorithm that achieves a 25x higher throughput compared to existing hierarchical methods. OmniActor, a generalist agent that leverages the synergy between GUI and embodied data to outperform agents trained on single modalities. PolicyEvolve, a framework for generating programmatic policies in multi-player games, which reduces reliance on manually crafted policy code and achieves high-performance policies with minimal environmental interactions.