<ul><li>Researchers propose Self-RedTeam, an online self-play reinforcement learning algorithm for safer language models.</li><li>The algorithm involves co-evolution of an attacker and defender agent through continuous interaction in a two-player zero-sum game.</li><li>Self-RedTeam enables dynamic co-adaptation and aims to converge to a Nash Equilibrium for reliable safety responses.</li><li>Empirical results show that Self-RedTeam uncovers more diverse attacks and achieves higher robustness on safety benchmarks compared to traditional static defender approaches.</li></ul>

Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models

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