Imagine a world where your virtual assistant understands your preferences, adapts to your needs, and provides you with a truly personalized experience. Or picture a language learning app that tailors its lessons to your unique learning style, ensuring you stay engaged and motivated throughout your journey. These are just a couple of the exciting possibilities that emerge when we combine reinforcement learning (RL) with speech and language technologies (SLT). The intersection of reinforcement learning (RL) and speech and language technologies (SLT) has given rise to a new paradigm in creating autonomous, adaptive, and intelligent systems that can learn and improve through interaction with their environments. In this chapter, we’ll introduce you to the fascinating world of RL+SLT, a new paradigm that’s set to revolutionize the way we design and deploy speech and language systems in real-world, real-time scenarios, as well as the way we interact with technology. By leveraging the power of RL as a universal learning mechanism, these systems can continuously adapt and optimize their behavior based on feedback and rewards, leading to improved performance and user experience. We’ll explore the key concepts behind RL+SLT, share some captivating real-world examples, and discuss the challenges and opportunities that lie ahead. Welcome, and get ready to embark on an exciting adventure into the future of speech and language technologies.