Safe reinforcement learning (RL) has emerged as a key paradigm for deploying AI in high-stakes domains such as autonomous driving, robotics, healthcare, and recommender systems. By embedding explicit constraints into the learning process, safe RL enables agents to optimize performance while satisfying critical requirements, including collision avoidance, resource limits, and system reliability. Such guarantees are indispensable for real-world AI, where failures can cause physical harm, economic loss, or loss of trust. At the same time, demand for trustworthy AI continues to grow as machine learning is increasingly deployed in human-centered applications. This makes it essential to design RL algorithms that are not only efficient but also reliable, robust, and aligned with societal needs.

This talk will survey recent progress on the design of safe and efficient RL algorithms with theoretical guarantees, focusing on both online and offline settings. I will begin by outlining the fundamental differences between standard RL and safe RL, highlighting unique challenges such as the absence of an optimality Bellman equation, which necessitates stochastic policies, and the impracticality of assuming full dataset coverage in offline settings. These structural gaps underscore the need for new algorithms that provide both efficiency and rigorous safety guarantees.