There are Levels to It: Red Teaming Dialogue With Hierarchical Reinforcement Learning

2026-10-15 · ArXiv

arXiv:2508.04451v2 Announce Type: replace-cross

Abstract: Red teaming is essential for securing Large Language Models, yet current automated methods remain limited by templates and single-turn attacks. To simulate the complex, interactive nature of real-world adversarial attacks, we introduce a novel red teaming paradigm designed to maximize expected cumulative harm through strategic interaction. By formalizing red teaming as a Markov Decision Process in a hierarchical reinforcement learning framework, we navigate the challenges of sparse rewards and long-horizon planning. Our generative agent learns diverse, multi-turn attacks using a token-level harm reward, consistently uncovering vulnerabilities that bypass baselines. This approach achieves a new state of the art and reframes LLM red teaming as a principled, trajectory-based process.

原文链接：https://arxiv.org/abs/2508.04451

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