Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents

The paper introduces AdvRole, an adversarial closed‑loop curriculum for training role‑playing agents with large language models. It alternates between an Actor that learns to role‑play and a Rewriter that edits character profiles and dialogue contexts into hard scenarios, using a performance‑gap reward to target the Actor’s weaknesses. Experiments on English, Chinese, and a new multilingual benchmark demonstrate that AdvRole consistently outperforms baseline methods.

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• llms
• agents
• reinforcement-learning
• benchmarks
• safety