The recent advancements in the field of embodied decision-making and reinforcement learning are pushing the boundaries of generalizability, scalability, and robustness. Researchers are focusing on developing world models that can effectively simulate and predict environments, enhancing decision-making processes in both simulated and real-world scenarios. Techniques such as behavior-conditioning and retracing-rollout are being integrated into scalable models like Whale-ST and Whale-X, demonstrating significant improvements in generalizability and uncertainty estimation. Additionally, novel state representation methods, such as State Chrono Representation (SCR), are addressing the limitations of traditional metric learning approaches by incorporating long-term temporal information, leading to better generalization in complex tasks. The field is also witnessing innovations in learning diverse and high-quality behaviors from limited demonstrations, with methods like Wasserstein Quality Diversity Imitation Learning (WQDIL) showing promising results in achieving near-expert performance. Furthermore, non-adversarial approaches to inverse reinforcement learning are emerging, offering more stable and efficient solutions by directly optimizing policy through successor feature matching. These developments collectively indicate a shift towards more robust, scalable, and diverse learning paradigms in embodied decision-making and reinforcement learning.