Nathan Lambert Releases New Textbook on LLM Post-Training

Prominent AI researcher Nathan Lambert has announced the release of his new textbook focusing on the practical methodologies and lessons learned from post-training open large language models (LLMs). The book compiles years of experience in training open models, covering key topics such as RLHF, alignment, and instruction tuning. Post-training is a critical phase that transforms raw, pre-trained LLMs into safe, helpful, and instruction-following assistants. This textbook provides AI and machine learning practitioners with a valuable, structured resource for building and optimizing open-source models. The textbook covers essential post-training techniques including Reinforcement Learning from Human Feedback (RLHF), instruction tuning, and alignment methodologies. It aims to bridge the gap between theoretical machine learning concepts and the practical engineering required to deploy open models.