ChARM: Character-based Act-adaptive Reward Modeling for Advanced
  Role-Playing Language Agents
Role-Playing Language Agents (RPLAs) aim to simulate characters for realistic
and engaging human-computer interactions. However, traditional reward models
often struggle with scalability and adapting to subjective conversational
preferences. We propose ChARM, a Character-based Act-adaptive Reward Model,
addressing these challenges through two innovations: (1) an act-adaptive margin
that significantly enhances learning efficiency and generalizability, and (2) a
self-evolution mechanism leveraging large-scale unlabeled data to improve
training coverage. Additionally, we introduce RoleplayPref, the first
large-scale preference dataset specifically for RPLAs, featuring 1,108
characters, 13 subcategories, and 16,888 bilingual dialogues, alongside
RoleplayEval, a dedicated evaluation benchmark. Experimental results show a 13%
improvement over the conventional Bradley-Terry model in preference rankings.
Furthermore, applying ChARM-generated rewards to preference learning techniques
(e.g., direct preference optimization) achieves state-of-the-art results on
CharacterEval and RoleplayEval. Code and dataset are available at
https://github.com/calubkk/ChARM.
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Feiteng Fang
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2025-06-10T09:29:29.714Z
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/avatars/9fac134537e6ef802f26d6d54c0e0bca.svg
feitengfang
user
feltoner
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Ting-En Lin
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Yuchuan Wu
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Xiong Liu
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Xiang Huang
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Dingwei Chen
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Jing Ye
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Haonan Zhang
claimed_verified
2025-08-19T14:07:03.399Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/623437bd4f78e3acb8bd14bd/6ZIRpE9We4TiMtzDBtPFS.jpeg
Haonan Zhang
user
haonanzhang
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Liang Zhu
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Hamid Alinejad-Rokny
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Min Yang
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Fei Huang
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Yongbin Li
ChARM: Character-based Act-adaptive Reward Modeling for Advanced
  Role-Playing Language Agents
Role-Playing Language Agents (RPLAs) aim to simulate characters for realistic
and engaging human-computer interactions. However, traditional reward models
often struggle with scalability and adapting to subjective conversational
preferences. We propose ChARM, a Character-based Act-adaptive Reward Model,
addressing these challenges through two innovations: (1) an act-adaptive margin
that significantly enhances learning efficiency and generalizability, and (2) a
self-evolution mechanism leveraging large-scale unlabeled data to improve
training coverage. Additionally, we introduce RoleplayPref, the first
large-scale preference dataset specifically for RPLAs, featuring 1,108
characters, 13 subcategories, and 16,888 bilingual dialogues, alongside
RoleplayEval, a dedicated evaluation benchmark. Experimental results show a 13%
improvement over the conventional Bradley-Terry model in preference rankings.
Furthermore, applying ChARM-generated rewards to preference learning techniques
(e.g., direct preference optimization) achieves state-of-the-art results on
CharacterEval and RoleplayEval. Code and dataset are available at
https://github.com/calubkk/ChARM.
ChARM, a character-focused adaptive reward model, improves preference learning for role-playing language agents by using an act-adaptive margin and self-evolution with unlabeled data, achieving superior results on dedicated benchmarks.
Role-Playing Language Agents ChARM act-adaptive margin self-evolution mechanism RoleplayPref RoleplayEval preference learning direct preference optimization CharacterEval