Get The Stanford RL Watch Guide

The companion to “ChatGPT Wasn’t Programmed to Think. It Was Rewarded.” PAGE 1 — the lecture link (Lecture 4, Reinforcement Learning with LLMs, 1:43:26, taught by Afshine and Shervine Amidi) and a 70-minute watch plan: what to watch, what to skim, and the one 86-second moment to catch. PAGES 2–4 — all 20 chapters with a clickable timestamp, a WATCH or SKIM verdict and plain-English notes. PAGE 5 — the 3 problems and the 4 methods (REINFORCE, PPO, GRPO, on-policy distillation) on one page: what each fixes and what it costs. PAGE 6 — the lecture’s Teddy-in-a-sealed-box example walked through every method. PAGE 7 — reward models, advantage, clipping, the KL penalty, the group average, the answer-length bias and its fixes (DAPO, Dr. GRPO), reverse KL, self-distillation and multi-teacher, in plain words, plus RLHF vs RLVR. PAGE 8 — the five questions from the video with full answers, and a glossary. Every note cites the lecture timestamp. Facts checked on October 20, 2026. Independent guide from Hyperautomation Labs — not affiliated with Stanford, Stanford Online or the instructors.

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