This research introduces Rational, a framework that enhances LLM safety through reasoning-based approaches rather than simple refusal mechanics.

• Addresses the limitations of traditional safety measures that rely on rigid refusal patterns
• Trains models to explain their safety decisions with explicit reasoning steps
• Improves model robustness against complex jailbreak attempts
• Creates more interpretable safety mechanisms for better trust and debugging

This approach matters for security because it represents a fundamental shift from binary block/allow decisions to nuanced understanding of safety contexts, making LLMs more resistant to sophisticated attacks while providing transparency into their decision-making process.

Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety