Learn how to optimize reinforcement learning training processes through practical techniques and proprietary stack solutions in this 20-minute conference talk. Discover the challenges of building superhuman agents specialized for particular tasks and understand how RL serves as a fundamental building block for delivering automated business value. Explore the iterative approach to RL training, including derisking runs with different base models and executing "hero" runs with optimal configurations. Examine cost-effective strategies to avoid the time-consuming and expensive pitfalls of naive RL implementation. Gain insights into Applied Compute's proprietary RL stack and the specific methods they employ to achieve efficient model training while maintaining effectiveness in real-world applications.