This research introduces R2Vul, a novel approach that significantly improves LLMs' capability to detect software vulnerabilities through reinforced structured reasoning.

• Combines reinforcement learning with structured reasoning distillation to enhance vulnerability detection
• Addresses limitations of standard chain-of-thought approaches in security contexts
• Teaches LLMs to distinguish between well-founded security assessments and plausible but misleading ones
• Demonstrates superior performance in detecting real-world software vulnerabilities

For security professionals, this advancement represents a critical step toward more reliable automated vulnerability detection, potentially reducing false positives and providing more actionable security insights.

R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation