P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
Pinyi Zhang · Ting-En Lin · Yuchuan Wu · Jingyang Chen · Zongqi Wang · Hua Yang · Bing Zhao · Fei Huang · Yongbin Li · Kai Zhang
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Pinyi Zhang · Ting-En Lin · Yuchuan Wu · Jingyang Chen · Zongqi Wang · Hua Yang · Bing Zhao · Fei Huang · Yongbin Li · Kai Zhang
Xiaoqian Liu · Ke Wang · Yuchuan Wu · Fei Huang · Yongbin Li · Jianbin Jiao · Junge Zhang
Chonghua Liao · Ke Wang · Yuchuan Wu · Ruoran Li · Fei Huang · Yongbin Li
Role-playing agents (RPAs) require balancing multiple objectives, such as instruction following, persona consistency, and stylistic fidelity, which are not always perfectly aligned across different dimensions. While prior work has primarily relied on supervised fine-tuning or reinforcement learni...
Feiteng Fang · Dingwei Chen · Xiang Huang · Ting-En Lin · Yuchuan Wu · Xiong Liu · Jing Ye · Ziqiang Liu · Haonan Zhang · Liang Zhu · Hamid Alinejad-Rokny · Min Yang · Yongbin Li
Currently, most reinforcement learning tasks focus on domains like mathematics and programming, where verification is relatively straightforward. However, in subjective tasks such as role-playing, alignment techniques struggle to make progress, primarily because subjective reward modeling using t...
Guiyang Hou · Xiang Huang · Shangke Lyu · Yuchuan Wu · Weiyao Luo · Xinyu Mei · Yongliang Shen · Weiming Lu · Yongbin Li
Theory of Mind (ToM), the ability to infer others’ mental states from behavior, is pivotal for developing machines with human-level social intelligence. Existing methods endowing LLMs with ToM fall into two paradigms: training-free methods and those repurposing ToM evaluation benchmarks as traini...