10 papers
Reward Modeling from Natural Language Human Feedback
Zongqi Wang, Rui Wang, Yuchuan Wu +5
Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GRMs). Typically in pairwise rewar…
SCAN: Structured Capability Assessment and Navigation for LLMs
Zongqi Wang, Tianle Gu, Chen Gong +3
Evaluating Large Language Models (LLMs) has become increasingly important, with automatic evaluation benchmarks gaining prominence as alternatives to human evaluation. While existi…
P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
Pinyi Zhang, Ting-En Lin, Yuchuan Wu +7
Personalized alignment of large language models seeks to adapt responses to individual user preferences, typically via reinforcement learning. A key challenge is obtaining accurate…
From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation
Tianle Gu, Kexin Huang, Lingyu Li +6
Safety moderation is pivotal for identifying harmful content. Despite the success of textual safety moderation, its multimodal counterparts remain hindered by a dual sparsity of da…
S2J: Bridging the Gap Between Solving and Judging Ability in Generative Reward Models
Shaoning Sun, Jiachen Yu, Zongqi Wang +3
With the rapid development of large language models (LLMs), generative reward models (GRMs) have been widely adopted for reward modeling and evaluation. Previous studies have prima…
Probing the Robustness of Large Language Models Safety to Latent Perturbations
Tianle Gu, Kexin Huang, Zongqi Wang +7
Safety alignment is a key requirement for building reliable Artificial General Intelligence. Despite significant advances in safety alignment, we observe that minor latent shifts c…