5 useful things you'll learn in my new post-training textbook (shipping now!)
- ID
- 12702
- Status
- summarized
- Published
- 10 Aug 2026, 9:02 PM
- Fetched
- 10 Aug 2026, 10:25 PM
- Provider
- Interconnects
- Category
- research-analysis
- Original URL
- https://www.interconnects.ai/p/5-useful-things-youll-learn-in-my
- Source URL
- https://www.interconnects.ai/feed
Summary
- Score
- 6.5
- Created
- 10 Aug 2026, 10:27 PM
- Tags
- Audience
- ai_ml_learnersdevelopers
What happened
Nathan Lambert's post-training textbook 'Reinforcement Learning from Human Feedback: Aligning and Post-training LLMs' is now published by Manning and freely available online, accompanied by a 12-hour video course, slides, a codebase with exercises, and model comparison examples. It covers topics like rejection sampling, outcome reward models, and character training at a foundational level, targeting readers with a CS background rather than beginners. The print edition is 50% off until August 19 with code PBLambert.
Why it matters
If you're an AI/ML learner or developer moving from model usage to model fine-tuning, the free online book plus 12-hour course gives you a structured path into RLHF and post-training techniques that are otherwise thinly documented. The 50% discount code expires Aug 19, so decide before then if you want the print version.
Discussion angle
Which post-training techniques (rejection sampling, outcome reward models, character training) are actually worth trying for small teams fine-tuning open models, versus just using API-based models?