Enhancing Language Models through Self-Editing

This chapter explores a novel meta-learning strategy where a language model is developed using a dual-loop system that incorporates self-generated data. It emphasizes the innovative process of self-edit generation, leveraging reinforcement learning to optimize model performance through contextual data and synthetic augmentations. The discussion also contrasts in-context learning with test-time training, illustrating how these methods enhance reasoning capabilities and accuracy in language models.