The field of safe control and reinforcement learning in dynamical systems is rapidly advancing, with a focus on developing innovative methods to ensure safety and stability in complex systems. Recent research has explored the use of control barrier functions (CBFs) to provide mathematical safety guarantees, as well as the integration of CBFs with reinforcement learning (RL) to enable safe exploration and exploitation. Notably, the development of viscosity CBFs has bridged the gap between CBFs and Hamilton-Jacobi reachability frameworks, providing a more comprehensive understanding of safe control. Additionally, the application of Gaussian process implicit surfaces as CBFs has shown promise in safe robot navigation. The use of adaptive action scaling in constraint-aware RL has also demonstrated significant reductions in constraint violations while maintaining task performance. Overall, the field is moving towards the development of more robust and efficient methods for safe control and RL, with potential applications in a wide range of areas, including robotics, autonomous systems, and building energy management. Noteworthy papers include: Viscosity CBFs: Bridging the Control Barrier Function and Hamilton-Jacobi Reachability Frameworks in Safe Control Theory, which introduces viscosity CBFs and their connection to CB-VFs. CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions, which proposes a framework for generating safe behaviors with RL by enforcing CBFs in training.