Online Alignment

Online alignment of large language models (LLMs) focuses on iteratively refining model behavior to better match human preferences through continuous interaction and feedback, unlike offline methods relying on static datasets. Current research emphasizes efficient online preference tuning algorithms, often employing bilevel optimization or self-play techniques to improve data exploration and reduce computational costs. This active learning approach aims to create more aligned and capable LLMs by dynamically adapting to evolving human preferences, leading to more robust and beneficial AI systems. The resulting improvements in alignment efficiency and performance have significant implications for the development and deployment of safe and helpful LLMs.