most citedImproving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

3 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

FlexAttention for Efficient High-Resolution Vision-Language Models

Junyan Li, Delin Chen, Tianle Cai +5

Current high-resolution vision-language models encode images as high-resolution image tokens and exhaustively take all these tokens to compute attention, which significantly increa…

cs.LG2024★ 1 cited

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Zhiqing Sun, Longhui Yu, Yikang Shen +4

Current AI alignment methodologies rely on human-provided demonstrations or judgments, and the learned capabilities of AI systems would be upper-bounded by human capabilities as a…

cs.LG2024★ 3 cited

Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

Shun Zhang, Zhenfang Chen, Sunli Chen +3

Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values. However, RLHF relies on a reward model that is…

cs.CV2023★ 2 cited

CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding

Junyan Li, Delin Chen, Yining Hong +4

A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundatio…

cs.CL2023

Autonomous Tree-search Ability of Large Language Models

Zheyu Zhang, Zhuorui Ye, Yikang Shen +1

Large Language Models have excelled in remarkable reasoning capabilities with advanced prompting techniques, but they fall short on tasks that require exploration, strategic foresi…

cs.RO2023★ 2 cited

Adaptive Online Replanning with Diffusion Models

Siyuan Zhou, Yilun Du, Shun Zhang +5

Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performanc…