This repository aggregates the most influential works on post-training large language models, covering techniques like supervised fine-tuning, reinforcement learning from human feedback (RLHF), and reasoning enhancement. It serves as both a survey referenced by an arXiv paper and a practical guide for researchers and practitioners working on training and improving LLMs after pre-training.