Reinforcement Learning from Human Feedback (RLHF)
by DeepLearning.AI × Google Cloud
Understand the technique that turned raw language models into helpful assistants.
Overview
RLHF is how base models are aligned to be helpful and safe. This short course explains the pipeline — collecting human preference data, training a reward model, and fine-tuning the policy with reinforcement learning — and has you run an example on the Google Cloud tooling. It gives you the conceptual map you need before diving into hands-on preference tuning with DPO/PPO elsewhere.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- ~1 hour
- Provider
- DeepLearning.AI × Google Cloud
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓The RLHF pipeline: preferences, reward model, policy tuning
- ✓Why alignment differs from ordinary fine-tuning
- ✓Running an RLHF example end to end
- ✓How to evaluate aligned models
Highlights
- •Demystifies the technique behind modern assistants
- •Conceptual clarity plus a hands-on lab
Who It’s For
Best For
- ✓Anyone wanting to understand model alignment
Prerequisites
- •Basic ML and fine-tuning concepts
FAQ
What is Reinforcement Learning from Human Feedback (RLHF)?
A conceptual and hands-on intro to RLHF — the alignment technique behind ChatGPT-style assistants — using open tooling.
Is Reinforcement Learning from Human Feedback (RLHF) free?
Reinforcement Learning from Human Feedback (RLHF) is free to access.
What level is Reinforcement Learning from Human Feedback (RLHF) for?
Reinforcement Learning from Human Feedback (RLHF) is aimed at a intermediate audience. Recommended background: Basic ML and fine-tuning concepts.
How long does Reinforcement Learning from Human Feedback (RLHF) take?
Expect roughly ~1 hour. Most learners work through it at their own pace.
What will I learn from Reinforcement Learning from Human Feedback (RLHF)?
You'll learn: The RLHF pipeline: preferences, reward model, policy tuning; Why alignment differs from ordinary fine-tuning; Running an RLHF example end to end; How to evaluate aligned models.