The recent advancements in the field of safe reinforcement learning (RL) have been notably focused on enhancing safety guarantees and robustness across various environments and control policies. A significant trend is the development of adaptive safety frameworks that can dynamically adjust to novel obstacles and unmodeled dynamics, ensuring safety without prior knowledge of the environment or specific controllers. These frameworks leverage observation-conditioned reachability to predict safety value functions in real-time, enabling rapid adaptation and robust safety enforcement. Additionally, there is a growing emphasis on addressing the limitations of traditional expectation-based safety constraints by introducing quantile-constrained RL, which provides higher safety assurance through direct quantile gradient estimation and asymmetric distributional updates. Another notable innovation is the integration of physics-model-guided worst-case sampling strategies into RL training, which significantly improves data efficiency and robustness by focusing on safety-critical corner cases. These approaches collectively push the boundaries of safe RL, making it more applicable to real-world scenarios with complex and unpredictable dynamics.

Noteworthy papers include one proposing an observation-conditioned reachability-based safety filter that adapts to novel environments and unmodeled dynamics, and another introducing a quantile-constrained RL method that enhances safety through direct quantile gradient updates.