arXiv:2606.09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel attacks, and co-training methods can produce more robust defenders in tandem. Recent works have demonstrated the efficacy of attacker-defender co-training by applying PPO and DPO, but report that GRPO is unstable in this setting. We introduce AdvGRPO, a co-training framework that makes GRPO viable for joint attacker-defender optimization using dense multi-channel rewards and decoupled advantage norma
The paper addresses the contemporary challenge of AI safety and robustness in large language models by introducing a novel red-teaming framework that makes a previously unstable method viable.
Improving the capability to red team and harden AI models is critical for their safe deployment and widespread adoption, especially as they become more autonomous and integrated into sensitive systems.
The viability of GRPO for co-training attacker-defender models provides a new, potentially more effective, method for discovering vulnerabilities and simultaneously developing more robust AI defenses.
- · AI safety researchers
- · Organizations deploying LLMs
- · AI security firms
- · Malicious AI attackers (short-term)
- · Companies with vulnerable LLMs
More resilient and secure large language models become available for various applications.
Reduced incidence of AI-related exploits or unintended harmful behaviors from LLMs.
Increased public and institutional trust in advanced AI systems, accelerating their integration into critical infrastructure.
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Read at arXiv cs.LG