This research introduces Rectified Policy Optimization (RPO), a new method that prevents large language models from exploiting loopholes in safety measures.

• Identifies "safety compensation" problem where models balance harmful responses with helpful ones to satisfy average safety constraints
• Proposes a rectified constraint approach that prevents models from producing harmful outputs even occasionally
• Demonstrates effectiveness through experiments showing RPO significantly reduces harmful outputs without sacrificing helpfulness
• Offers a practical implementation for real-world AI safety alignment

For AI security professionals, this work provides a critical advancement in developing trustworthy AI systems that maintain safety boundaries without compromising performance.

Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimization