RLHF Book Course videos

Reinforcement learning from human feedback is the training step that turns a raw language model into a chatbot that follows instructions, and a new RLHF Book course has started packaging that process as a repeatable workflow. (rlhfbook.com 1) (rlhfbook.com 2) Nathan Lambert’s RLHF Book site says it launched a course page with lecture videos in March 2026, and by April 12 the site listed four lectures in order, starting with “Overview” and then “IFT, Reward Models, & Rejection Sampling.” (rlhfbook.com 1) (rlhfbook.com 2) The accompanying YouTube playlist says the lectures were made to “accompany and add onto” the online book and print edition. The book’s GitHub repository shows active updates on April 14, 2026, and describes the project as “a comprehensive guide” to RLHF and post-training language models. (youtube.com) (github.com) The technical idea is simple to state and hard to run: first teach a model with examples, then collect comparisons between outputs, then train a scoring system that predicts which answer people prefer, then use that score to tune the model again. OpenAI’s InstructGPT paper laid out that sequence in 2022 as supervised fine-tuning, reward modeling, and reinforcement learning. (arxiv.org) Lambert’s book frames that sequence as “the core optimization stages” of post-training, running from instruction tuning to reward models to rejection sampling, reinforcement learning, and direct alignment algorithms. The course page maps those steps into separate lectures instead of treating alignment as a single last-mile patch. (rlhfbook.com 1) (rlhfbook.com 2) That reflects how the field has broadened since ChatGPT-era papers made RLHF famous. The RLHF Book homepage now describes itself not just as a guide to reinforcement learning from human feedback, but also as “a short introduction to RLHF and post-training focused on language models.” (rlhfbook.com) The book also spends unusual space on the operational bottleneck: preference data. Its chapter on that topic says human preferences cannot be written down as a clean reward function, so teams use comparison data as a proxy, and collecting that data can cost “hundreds of thousands (or millions of dollars).” (rlhfbook.com) That emphasis moves the conversation away from one algorithm and toward a production system with data collection, model tuning, and evaluation loops. Lambert’s GitHub README says one reason for writing the book was that methods such as rejection sampling lacked a canonical reference and some industry practices had “no open research.” (github.com) The newer vocabulary also leaves room for methods that do not rely on humans at every step. Anthropic’s 2022 Constitutional AI paper described a pipeline where a model generates critiques and revisions, then a preference model is trained from AI-generated comparisons before reinforcement learning. (anthropic.com) So the news in these videos is less a new algorithm than a new attempt to standardize the map. As more labs talk about post-training as a stack of data, reward, and evaluation systems, the field is starting to teach RLHF the way engineering teams already run it. (rlhfbook.com) (github.com)