Module 3: Reinforcement Learning from Human Feedback

episode

The AI Concepts Podcast not yet transcribed

▲ 0

Transcript

This episode hasn't been transcribed yet Upvotes decide what gets transcribed next — 0 so far. Sign in to upvote

Related

recent transcribed audio

Your partner wants profit. You want freedom. Now what? 22 min · 16 Sep 2026 · The Optometry Success Podcast

Board of County Supervisors Meeting - Work Session - Sep 15, 2026 3h 17m · 15 Sep 2026 · Prince William County Board of Supervisors

Finance and Budget Committee - Sep 15, 2026 1h 43m · 15 Sep 2026 · Prince William County Finance and Budget Committee

Sept. 15, 2026 Board of Supervisors Meeting 8h 46m · 15 Sep 2026 · Fairfax County Board of Supervisors

The Art of True Happiness | Dr. Arthur Brooks 2h 49m · 14 Sep 2026 · Huberman Lab

The Chris Wolfe Podcast: The Power of Quality Supplements 48 min · 14 Sep 2026 · EyeCode Media

Report any issue

Source

Recorded20 Feb 2026

Publisherfeed.podbean.com ↗

Handlerec:16019846

Show notes

This episode addresses how Reinforcement Learning from Human Feedback (RLHF) adds the final layer of alignment after supervised fine-tuning, shifting the training signal from “right vs wrong” to “better vs worse.” We explore how preference rankings create a reward signal (reward models plus PPO) and the newer shortcut (DPO) that learns preferences directly, then connect RLHF to safety through the Helpful, Honest, Harmless goal. We also unpack the “alignment tax,” the trade-off between being safe and being genuinely useful, and close by setting up the next module on running models at scale, starting with GPU memory limits, plus a personal reflection on starting later without being behind.

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

Sign in