DuoGuard addresses the critical gap in multilingual safety for LLMs through an innovative two-player reinforcement learning framework where generator and guardrail models evolve adversarially.

• Creates effective safety guardrails across multiple languages despite limited non-English safety data
• Uses adversarial co-evolution between generator and guardrail models to improve detection capabilities
• Overcomes the scarcity of multilingual safety data through reinforcement learning
• Enhances security by identifying and filtering unsafe and illegal content in diverse linguistic contexts

This research is vital for security as it enables responsible AI deployment in global markets by ensuring consistent safety standards across languages, reducing harmful content risks in multilingual applications.

DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails