TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment
In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful content. The mainstream paradigm for LLM safety alignment typically adopts a collaborative framework involving three roles: an attacker for adversarial prompt generation, a defender for safety defense, and an evaluator for response assessment. In this paper, we propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative and co-improving collaboration among three roles with near-zero manual annotation. Experimental results show that the attacker preserves high output diversity while achieving a 20%-50% improvement in adversarial effectiveness; the defender attains 10%-30% gains in safety performance without degrading general reasoning capability; and the evaluator continuously refines its fine-grained judgment ability through iterations, accurately distinguishing unsafe responses, simple refusals, and useful guidance. Overall, our framework establishes an efficient and scalable paradigm for LLM safety alignment, enabling continuous co-evolution within a unified learning loop.
6979d0aedf44b75fa47e48d5
Zhewen Tan
6979d0aedf44b75fa47e48d6
Wenhan Yu
6979d0aedf44b75fa47e48d7
Jianfeng Si
6979d0aedf44b75fa47e48d8
Tongxin Liu
6979d0aedf44b75fa47e48d9
Kaiqi Guan
6979d0aedf44b75fa47e48da
Huiyan Jin
6979d0aedf44b75fa47e48db
Jiawen Tao
6979d0aedf44b75fa47e48dc
Xiaokun Yuan
6979d0aedf44b75fa47e48dd
Duohe Ma
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Xiangzheng Zhang
6979d0aedf44b75fa47e48df
Tong Yang
6979d0aedf44b75fa47e48e0
Lin Sun
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2026-01-28T11:12:39.873Z
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TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment
In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful content. The mainstream paradigm for LLM safety alignment typically adopts a collaborative framework involving three roles: an attacker for adversarial prompt generation, a defender for safety defense, and an evaluator for response assessment. In this paper, we propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative and co-improving collaboration among three roles with near-zero manual annotation. Experimental results show that the attacker preserves high output diversity while achieving a 20%-50% improvement in adversarial effectiveness; the defender attains 10%-30% gains in safety performance without degrading general reasoning capability; and the evaluator continuously refines its fine-grained judgment ability through iterations, accurately distinguishing unsafe responses, simple refusals, and useful guidance. Overall, our framework establishes an efficient and scalable paradigm for LLM safety alignment, enabling continuous co-evolution within a unified learning loop.
A closed-loop reinforcement learning framework enables iterative collaboration between attacker, defender, and evaluator roles for improved large language model safety alignment without manual annotations.
reinforcement learning large language models safety alignment adversarial prompt generation safety defense response assessment closed-loop framework co-improving collaboration iterative learning