  While Reinforcement Learning from Human Feedback (RLHF) significantly
enhances the generation quality of Large Language Models (LLMs), recent studies
have raised concerns regarding the complexity and instability associated with
the Proximal Policy Optimization (PPO) algorithm, proposing a series of
order-based calibration methods as viable alternatives. This paper delves
further into current order-based methods, examining their inefficiencies in
utilizing reward values and addressing misalignment issues. Building upon these
findings, we propose a novel \textbf{V}alue-based \textbf{C}ali\textbf{B}ration
(VCB) method to better align LLMs with human preferences. Experimental results
demonstrate that VCB surpasses existing alignment methods on AI assistant and
summarization datasets, providing impressive generalizability, robustness, and
stability in diverse settings.
