most citedToo Good to be Bad: On the Failure of LLMs to Role-Play Villains

2 citations · 2 across the 3 of their papers we have counts for

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cs.CL2026

Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning

Qiao Liang, Yuke Zhu, Chao Ge +4

Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning in this setting suffer…

cs.CL2025

Too Good to be Bad: On the Failure of LLMs to Role-Play Villains

Zihao Yi, Qingxuan Jiang, Ruotian Ma +8

Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, an…