6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.AI2026
ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training
Yu Liang, Liangxin Liu, Longzheng Wang +5
Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering greater representational capacity…
cs.AI2026
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework
Kai Qin, Liangxin Liu, Yu Liang +7
Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment quality of Large Language Models (…
cs.CL2024★ 6 cited
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation
Longzheng Wang, Xiaohan Xu, Lei Zhang +5
Automatic detection of multimodal misinformation has gained a widespread attention recently. However, the potential of powerful Large Language Models (LLMs) for multimodal misinfor…