Hao Wang, Peilin Zhao, Deheng Ye, Zheng Zhang, Liu Liu, Mao Zheng, Qi Chai
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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 between an Actor that learns to role-play and a Rewriter that edits character profiles and dialogue contexts into actor-specific hard scenarios. The Rewriter is trained with a performance-gap reward, which favors rewrites that reduce the current Actor's score relative to the original scenario. As a result, the scenario pool evolves with the Actor and continuously targets under-mastered regions of the character-context space. Experiments on three role-playing benchmarks covering English and Chinese, as well as a new multilingual benchmark we release, show that AdvRole consistently outperforms baselines.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2609.28609")
get_code_for_paper("2609.28609")
have("2609.28609")
The run record, dated, one paper per request, free:
curl https://syntology.ai/api/ran/2609.28609.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2609.28609)
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