February 7, 2026
Teach bots, stir pots
Reinforcement Learning from Human Feedback
New AI “training manual” drops: fans rush in while skeptics ask if it’s just dog treats for robots
TLDR: A new book explains how AI learns from human feedback, laying out the full process in plain language. The crowd loved the easy web version but argued whether this is real science or just polishing chatbot vibes, while the author’s open call for feedback keeps the debate hot and relevant.
Reinforcement Learning from Human Feedback (RLHF) just got the glossy, all-in-one guide treatment—and the crowd showed up with popcorn. Think of RLHF as teaching a chatbot with human hints: reward good answers, nudge away the weird ones. The book promises a gentle walk from origins to how-to, and the comments immediately went full “link or it didn’t happen” energy, with klelatti dropping the web version like it’s a mixtape.
The real drama? A split screen of vibes vs. rigor. Builders cheered having a step-by-step playbook (instruction tuning to reward models to the final alignment dance), while skeptics rolled their eyes, calling it “fancy duct tape” for chatbots—funny memes compared it to dog training and sticker charts. verdverm stirred the pot: the author, Nathan, is actively cooking a next version and asking for feedback, meaning this isn’t a museum piece, it’s a live remix. Then dang linked a related HN thread, officially turning it into a multi-ring nerdfight. As the book teases “synthetic data” (AI-made practice questions) and better evaluation, people argued whether training AI on fake human signals is genius or chaos. It’s informative, it’s messy, and yes—there are treats involved.
Key Points
- •The book introduces RLHF for readers with quantitative backgrounds.
- •It connects RLHF’s origins to recent literature and fields including economics, philosophy, and optimal control.
- •Foundational material covers definitions, problem formulation, data collection, and common mathematical tools.
- •Core RLHF stages include instruction tuning, reward modeling, rejection sampling, reinforcement learning, and direct alignment.
- •Advanced topics address synthetic data, evaluation, and open research questions in the field.