The recent developments in the field of reinforcement learning (RL) have shown a significant shift towards more adaptive, sample-efficient, and generalizable approaches. A notable trend is the integration of diffusion models and imitation learning techniques to address challenges in off-dynamics and continual RL, enhancing both stability and plasticity. Additionally, there is a growing focus on policy space compression and adaptive learning frameworks that guide end-to-end modeling for multi-stage decision-making, which promises to improve the efficiency and robustness of RL algorithms. The use of temporal Gaussian Mixture Models for structure learning in model-based RL and the alignment of few-step diffusion models with dense reward difference learning are also advancing the field by providing more sophisticated and adaptable learning mechanisms. Furthermore, the exploration of RL in real-world applications such as beamline alignment in synchrotron radiation sources demonstrates the practical utility and scalability of these methods. Overall, the field is moving towards more integrated, adaptive, and context-aware RL solutions that can handle complex, real-world problems more effectively.