The field of reinforcement learning and embodied AI is moving towards developing agents that can learn and adapt across diverse environments and tasks. A key challenge in this area is the lack of standardized, heterogeneous environments and unified evaluation metrics. Recent research has focused on addressing this gap by proposing automated frameworks for generating environments and developing new evaluation metrics. Another significant trend is the use of autonomous data generation methods, such as self-evolving data synthesis and reinforcement learning-based trajectory generation, to improve the quality and diversity of training data. These advances have the potential to enable more scalable and generalizable agent learning. Noteworthy papers in this area include AutoEnv, which proposes an automated framework for generating heterogeneous environments, and Syn-GRPO, which employs online data synthesis to improve data quality. Discover, Learn, and Reinforce is also notable for its information-theoretic approach to generating diverse trajectories for vision-language-action pretraining. Additionally, Robot-Powered Data Flywheels showcases the potential of deploying robots in the wild to collect real-world data and improve foundation model adaptation.