Unifying Data Selection and Self-Refinement for Post-Training LLMs
Published:Nov 26, 2025 04:48
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
Reference
“The paper focuses on post-training methods.”