Language Reward

Language reward research focuses on developing methods for training and aligning large language models (LLMs) using reward signals derived from language, rather than solely relying on human-labeled data. Current research emphasizes self-supervised learning techniques, including contrastive learning and iterative preference optimization, often employing LLMs themselves as meta-judges to assess and refine their own responses. This work aims to improve model alignment, efficiency, and generalization across diverse tasks, such as instruction following, robotic control, and multi-lingual applications. The resulting advancements have significant implications for creating more robust, efficient, and human-aligned AI systems.