Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
Quick summary
arXiv:2609.28609v1 Announce Type: new Abstract: Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning begins. This creates a distributional bottleneck: as the agent improves, the scenarios where it performs poorly also change, while the training distribution remains static. Therefore, we propose AdvRole, an adversarial context rewriting framework that turns role-playing RL into a closed-loop curriculum. AdvRole alternates bet
Key takeaways
- arXiv:2609.28609v1 Announce Type: new Abstract: Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation.
- Recent RL methods typically train on a fixed scenario pool collected before learning begins.
- This creates a distributional bottleneck: as the agent improves, the scenarios where it performs poorly also change, while the training distribution remains static.
Why it matters
The importance of “Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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