Reinforcement Learning from Human Feedback (RLHF)
by DeepLearning.AI × Google Cloud
Ninety minutes to actually understand reward models and policy tuning — then go learn DPO.
Overview
This DeepLearning.AI short course, built with Google Cloud and taught by developer advocate Nikita Namjoshi, walks through the classic RLHF pipeline end to end in 1 hour 32 minutes. Six video lessons plus four code examples cover Introduction (4m), How does RLHF work (11m), Datasets for RL training (9m), Tune an LLM with RLHF (24m), Evaluate the tuned model (22m) and Google Cloud Setup (10m), closing with a 10-minute graded quiz that requires a paid PRO membership. The hands-on portion fine-tunes Llama 2 using the open-source Google Cloud Pipeline Components Library, so you see the three-stage structure concretely: a preference dataset of chosen and rejected completions, a reward model trained to score them, and a policy tuned by reinforcement learning against that reward with a KL penalty holding it near the supervised checkpoint. You then compare the tuned model against the base model with a side-by-side evaluation. Be clear about where this sits now: RLHF remains the conceptual foundation of alignment, but teams increasingly reach instead for Direct Preference Optimization, which drops both the separate reward model and the RL loop and updates the model straight from chosen and rejected pairs, or for GRPO-style methods that score against verifiable rewards. The Llama 2 and Vertex pipeline tooling here is dated. Take this course for the mental model of reward modelling and policy optimization, then learn DPO or GRPO for the implementation you would actually ship.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- 1h32m across 6 lessons and 4 labs, self-paced
- Provider
- DeepLearning.AI × Google Cloud
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Distinguish the three RLHF stages: supervised tuning, reward model, policy optimization
- ✓Understand what a preference dataset of chosen and rejected completions contains
- ✓Train a reward model that scores completions the way annotators would
- ✓Run an RLHF tuning job with the Google Cloud Pipeline Components Library
- ✓Evaluate a tuned model side by side against its base checkpoint
- ✓Explain why a KL penalty keeps the policy near the supervised checkpoint
- ✓Judge when RLHF is warranted versus DPO or verifiable-reward methods
Highlights
- •Makes the reward-model-plus-policy structure concrete instead of hand-wavy
- •Runs a real managed pipeline on Llama 2, not a toy notebook simulation
- •Under 90 minutes — the fastest honest route to understanding alignment
- •Free to watch and run; only the graded quiz needs a paid PRO membership
- •Honest caveat: the Llama 2 and Google Cloud pipeline tooling is dated, and practice has moved toward DPO and GRPO
Who It’s For
Best For
- ✓Engineers who need the vocabulary of alignment before reading post-training papers
- ✓ML practitioners deciding between RLHF, DPO and verifiable-reward RL
- ✓Anyone evaluating a fine-tuning vendor who needs to ask the right questions
Prerequisites
- •Intermediate Python, per DeepLearning.AI's stated prerequisite
- •Basic machine-learning and fine-tuning concepts
- •A Google Cloud account if you want to run the pipeline outside the course notebooks
FAQ
What is Reinforcement Learning from Human Feedback (RLHF)?
A 1h32m DeepLearning.AI course, built with Google Cloud, on Reinforcement Learning from Human Feedback — the alignment pipeline that turned base models into assistants. Six lessons and four labs take you through preference datasets, reward-model training and policy tuning on Vertex AI, then evaluating the tuned model side by side against its base checkpoint.
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: Intermediate Python, per DeepLearning.AI's stated prerequisite, Basic machine-learning and fine-tuning concepts, A Google Cloud account if you want to run the pipeline outside the course notebooks.
How long does Reinforcement Learning from Human Feedback (RLHF) take?
Expect roughly 1h32m across 6 lessons and 4 labs, self-paced. Most learners work through it at their own pace.
What will I learn from Reinforcement Learning from Human Feedback (RLHF)?
You'll learn: Distinguish the three RLHF stages: supervised tuning, reward model, policy optimization; Understand what a preference dataset of chosen and rejected completions contains; Train a reward model that scores completions the way annotators would; Run an RLHF tuning job with the Google Cloud Pipeline Components Library; Evaluate a tuned model side by side against its base checkpoint; Explain why a KL penalty keeps the policy near the supervised checkpoint; Judge when RLHF is warranted versus DPO or verifiable-reward methods.
Topics
Sources
This page was written from 3 sources, 1 on domains other than deeplearning.ai.