2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Mitigating Exploration Bias in RL for Multi-Instruction Following
Mian Zhang, Yueqin Yin, Kaiyu He +4
RL has emerged as a powerful paradigm for enhancing the instruction following capabilities of LLMs. While existing training recipes achieve substantial gains, we find that they suf…
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…
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…
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…