Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
This work addresses alignment issues in large language models for applications like AI assistants and summarization, representing an incremental improvement over existing order-based methods.
The paper tackles the inefficiency and misalignment of order-based calibration methods in language model alignment by proposing a novel Value-based Calibration (VCB) method, which outperforms existing methods on AI assistant and summarization datasets with improved generalizability, robustness, and stability.
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.