Prototypical Reward Network for Data-Efficient RLHF

Jan 1, 2024·
Jinghan Zhang
,
Xiting Wang
Yiqiao Jin
Yiqiao Jin
,
Changyu Chen
,
Xinhao Zhang
,
Kunpeng Liu
· 1 min read
Abstract
We propose a prototypical reward network that enables data-efficient reinforcement learning from human feedback (RLHF) for large language models.
Type
Publication
ACL 2024 Main Conference, Oral Presentation

Abstract

We propose a prototypical reward network that enables data-efficient reinforcement learning from human feedback (RLHF) for large language models.

Keywords

Reinforcement Learning, Human Feedback, Large Language Models, Data Efficiency

Yiqiao Jin
Authors
Final-year Ph.D. Candidate in Computer Science
My research sits at the intersection of multimodal foundation models, intelligent agents, and spatial/physical intelligence. I build general-purpose models that perceive, reason, learn, and act in interactive virtual and physical environments.