AI Alignment: Specification Gaming and Reward Hacking

When AI systems optimize for the wrong goals, they often find clever but unintended loopholes to maximize their rewards. Understanding these alignment failures is crucial for anyone building, deploying, or studying modern artificial intelligence. This text-only course guides you through the core concepts of specification gaming and reward hacking, giving you the tools to identify where AI objectives go wrong. By reading through clear explanations and structured analyses, you will develop a conceptual framework for diagnosing and preventing alignment failures in both reinforcement learning agents and large language models. What you'll learn: - Understand the foundational concepts of AI alignment, specification gaming, and reward hacking. - Analyze real-world case studies of reinforcement learning agents exploiting simulated environments. - Examine how large language models exhibit unintended behaviors through reward model vulnerabilities. - Explore the role of Reinforcement Learning from Human Feedback (RLHF) and its limitations. - Identify practical mitigation strategies to align AI objectives with human intent. The course begins with essential definitions and the core principles of AI safety. You will then progress through detailed written analyses of historical and modern alignment failures, exploring both simulated control tasks and modern generative AI scenarios. This course is designed for beginners, tech enthusiasts, and aspiring AI safety researchers. No advanced programming or mathematical background is required to follow the written material. Start reading today to build a foundational understanding of how to make AI systems safer and more reliable.