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

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

9 Jun 2025arXiv:2506.07468archive 2025-07-28

Mickel Liu, Liwei Jiang, Yancheng Liang, Simon Shaolei Du, Yejin Choi, Tim Althoff, Natasha Jaques

Conventional language model (LM) safety alignment relies on a reactive, disjoint procedure: attackers exploit a static model, followed by defensive fine-tuning to patch exposed vulnerabilities. This sequential approach creates a mismatch -- attackers overfit to obsolete defenses, while defenders perpetually lag behind emerging threats. To address this, we propose Self-RedTeam, an online self-play reinforcement learning algorithm where an attacker and defender agent co-evolve through continuous interaction. We cast safety alignment as a two-player zero-sum game, where a single model alternates between attacker and defender roles -- generating adversarial prompts and safeguarding against them -- while a reward LM adjudicates outcomes. This enables dynamic co-adaptation. Grounded in the game-theoretic framework of zero-sum games, we establish a theoretical safety guarantee which motivates the design of our method: if self-play converges to a Nash Equilibrium, the defender will reliably produce safe responses to any adversarial input. Empirically, Self-RedTeam uncovers more diverse attacks (+21.8% SBERT) compared to attackers trained against static defenders and achieves higher robustness on safety benchmarks (e.g., +65.5% on WildJailBreak) than defenders trained against static attackers. We further propose hidden Chain-of-Thought, allowing agents to plan privately, which boosts adversarial diversity and reduces over-refusals. Our results motivate a shift from reactive patching to proactive co-evolution in LM safety training, enabling scalable, autonomous, and robust self-improvement of LMs via multi-agent reinforcement learning (MARL).

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mickelliu/selfplay-redteaming officialmentioned in papermentioned on GitHubpytorch report

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1ran · honoured contract
5ran · our draft was wrong
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cot_format_check_and_extract mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository ran · our draft was wrong Apache-2.0 (permissive) · e5918ff1278852d7 · report
get_attacker_prompt_reward mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository ran · our draft was wrong Apache-2.0 (permissive) · ee235fd9a7e3e346 · report
get_cot_formatting_reward mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository ran · honoured contract fingerprinted Apache-2.0 (permissive) · c076751eec7d6013 · report
get_response_harmfulness_reward mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository ran · our draft was wrong Apache-2.0 (permissive) · e9f51494b44f4e8e · report
get_response_refusal_reward mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 9b99eb231136dd0e · report
DialogueGameManager mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · c8f1077d707eb877 · report
GameOutcome mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · 248607c655d8ea9c · report
PromptHarmfulness mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · 28badd08d6d3c33f · report
ResponseHarmfulness mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · d705dafdd918f797 · report
ResponseRefusal mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · 1ed39104ef491246 · report
convert_game_history_to_messages mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · f0eec14ba1e2868d · report
convert_game_history_to_query mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · b3c0cd514922498f · report
get_redteaming_game_reward_general_sum mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · cea8712a2eeceb41 · report
get_redteaming_game_reward_zero_sum mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · 6070c9f7525de92f · report
remote_rm_fn mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · 3417fd8982acd9e0 · report
request_api_wrapper mickelliu/selfplay-redteaming/openrlhf/trainer/ppo_utils/language_game.py official repository unverified Apache-2.0 (permissive) · e906613b41734aa5 · report
request_caching_arg_to_dict identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · c44830a7722ff5f7 · report

Tasks

Multi-agent Reinforcement LearningSafety Alignment

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Methods

Patching

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