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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.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…
cs.CL2024
Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts
Yueqin Yin, Zhendong Wang, Yi Gu +3
In the field of large language models (LLMs), aligning models with the diverse preferences of users is a critical challenge. Direct Preference Optimization (DPO) has played a key r…