3 papers
cs.CL2025
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
Yueqin Yin, Shentao Yang, Yujia Xie +5
Reinforcement learning from human feedback (RLHF) has been widely adopted to align language models (LMs) with human preference. Prior RLHF works typically take a bandit formulation…
cs.CV2024
Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization
Yi Gu, Zhendong Wang, Yueqin Yin +2
Aligning large language models with human preferences has emerged as a critical focus in language modeling research. Yet, integrating preference learning into Text-to-Image (T2I) g…
cs.CL2024
Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment
Yueqin Yin, Zhendong Wang, Yujia Xie +2
Traditional language model alignment methods, such as Direct Preference Optimization (DPO), are limited by their dependence on static, pre-collected paired preference data, which h…