13 citations · 40 across the 21 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Reward Modeling via Iterative Self-Training
Yifei He, Haoxiang Wang, Ziyan Jiang +2
Reward models (RM) capture the values and preferences of humans and play a central role in Reinforcement Learning with Human Feedback (RLHF) to align pretrained large language mode…
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts
Haoxiang Wang, Wei Xiong, Tengyang Xie +2
Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences. The RLHF process typically sta…
RLHF Workflow: From Reward Modeling to Online RLHF
Hanze Dong, Wei Xiong, Bo Pang +7
We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpa…
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience
Xiaohang Yu, Zhengxian Yang, Shi Pan +8
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for vario…
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Haoxiang Wang, Yong Lin, Wei Xiong +5
Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human…
Enhancing Compositional Generalization via Compositional Feature Alignment
Haoxiang Wang, Haozhe Si, Huajie Shao +1
Real-world applications of machine learning models often confront data distribution shifts, wherein discrepancies exist between the training and test data distributions. In the com…