Reinforcement learning (RL) is a versatile approach that finds application in various fields, particularly in creating reliable AI agents. The "Practitioner’s guide to reinforcement learning" offers insights into its practical usage. It highlights how RL operates in post-training scenarios and contrasts it with supervised fine-tuning (SFT). The guide emphasizes actionable patterns that can transition from research settings to real-world applications, making it relevant for professionals looking to implement RL effectively. One of the key advancements discussed is the LoRA technique, which improves fine-tuning efficiency, allowing for more effective model adjustments without extensive resource use. The guide also outlines several important benefits and use cases for RL in production environments, demonstrating its impact on enhancing operational workflows. A new feature from Weights & Biases, Serverless RL, is introduced, which simplifies the deployment of RL models, making it easier for practitioners to integrate these technologies into their systems. The article also shares success stories from companies like Square and Canva. Square uses RL to speed up the development of new language models for its Square Assistant, enhancing conversational AI capabilities. Canva, on the other hand, leverages Weights & Biases for MLOps, facilitating a smooth transition from experimentation to deployment. This integration supports over 150 million users by improving AI-driven design tools. Leonardo.ai collaborates with AWS and Weights & Biases to enhance its GenAI platform, allowing creators to generate high-quality art assets efficiently. These examples illustrate how RL and related technologies are reshaping industries by democratizing access to advanced AI tools.