Unifying Data Selection and Self-Refinement for Post-Training LLMs

This ArXiv paper explores a crucial area for improving the performance of Large Language Models (LLMs) after their initial training. The research focuses on methods to refine and optimize LLMs using offline data selection and online self-refinement techniques.