The research area is witnessing significant advancements in the application of transformers and model-based reinforcement learning (MBRL) for complex decision-making tasks. A notable trend is the development of novel training objectives and inference techniques that enhance transformers' long-term planning capabilities, particularly in maze navigation and trajectory prediction. These innovations are not only improving sample efficiency and convergence rates but also demonstrating superior performance over traditional methods. In the realm of MBRL, there is a growing focus on mitigating distractions and irrelevant details that can hinder policy optimization, with new methods leveraging pretrained models and adversarial learning to maintain focus on task-relevant dynamics. Additionally, advancements in offline reinforcement learning are addressing distribution mismatch challenges through innovative frameworks that refine policies using synthetic experiences, enhancing both sample efficiency and robustness. Multi-agent reinforcement learning is also progressing with algorithms that optimize coordination and avoid out-of-distribution actions, ensuring more effective policy updates. Lastly, the paradigm of online reinforcement learning fine-tuning is evolving with approaches that eliminate the need for retaining offline data, thereby accelerating learning and improving performance. Overall, these developments are pushing the boundaries of what is possible in automated decision-making and policy optimization.