RLHF 101: A Technical Tutorial

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A CMU ML blog tutorial useful for those seeking a technical grounding in RLHF methods; relevant background for understanding alignment approaches used in deployed LLMs like ChatGPT and Claude.

Metadata

Importance: 62/100blog posteducational

Summary

A technical tutorial from CMU's ML blog covering the foundations and mechanics of Reinforcement Learning from Human Feedback (RLHF), including reward modeling, policy optimization, and alignment objectives. It provides an accessible yet rigorous introduction to how RLHF is used to align large language models with human preferences. The tutorial bridges theory and practice for researchers and practitioners entering the field.

Key Points

•Covers the core RLHF pipeline: supervised fine-tuning, reward model training from human comparisons, and RL-based policy optimization (e.g., PPO).
•Explains how human preference data is collected and used to train a reward model that proxies human judgment.
•Discusses key challenges in RLHF including reward hacking, overoptimization, and distribution shift between the reference and trained policy.
•Provides technical grounding in the KL-divergence penalty used to prevent the policy from deviating too far from the base model.
•Serves as an educational reference for understanding why RLHF has become a dominant alignment technique in modern LLM development.

Cited by 1 page

Page	Type	Quality
RLHF	Research Area	63.0

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RLHF 101: A Technical Tutorial on Reinforcement Learning from Human Feedback &#8211; Machine Learning Blog | ML@CMU | Carnegie Mellon University 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 

 
 
 
 
 
 
 

 

 

 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 RLHF 101: A Technical Tutorial on Reinforcement Learning from Human Feedback 

 
 
 Authors

 Zhaolin Gao and Gokul Swamy 
 by Zhaolin Gao 
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 Published

 June 1, 2025 
 
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 Reinforcement Learning from Human Feedback (RLHF) is a popular technique used to align AI systems with human preferences by training them using feedback from people, rather than relying solely on predefined reward functions. Instead of coding every desirable behavior manually (which is often infeasible in complex tasks) RLHF allows models, especially large language models (LLMs), to learn from examples of what humans consider good or bad outputs. This approach is particularly important for tasks where success is subjective or hard to quantify, such as generating helpful and safe text responses. RLHF has become a cornerstone in building more aligned and controllable AI systems, making it essential for developing AI that behaves in ways humans intend.

 This blog dives into the full training pipeline of the RLHF framework. We will explore every stage — from data generation and reward model inference, to the final training of an LLM. Our goal is to ensure that everything is fully reproducible by providing all the necessary code and the exact specifications of the environments used. By the end of this post, you should know the general pipeline to train any model with any instruction dataset using the RLHF algorithm of your choice!

 Preliminary: Setup & Environment

 We will use the following setup for this tutorial:

 Dataset: UltraFeedback , a well-curated dataset consisting of general chat prompts. (While UltraFeedback also contains LLM-generated responses to the prompts, we won&#8217;t be using these.)
 Base Model: Llama-3-8B-it , a state-of-the-art instruction-tuned LLM. This is the model we will fine-tune.
 Reward Model: Armo , a robust reward model optimized for evaluating the generated outputs. We will use Armo to assign scalar reward values to candidate responses, indicating how &#8220;good&#8221; or &#8220;aligned&#8221; a response is.
 Training Algorithm: REBEL , a state-of-the-art algorithm tailored for efficient RLHF optimization.
 

 To get started, clone our repo, which contains all the resources required for this tutorial:

 git clone https://github.com/ZhaolinGao/REBEL
cd REBEL 

 We use two separate environments for 

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Resource ID: bbc6c3ef9277667e | Stable ID: ZmJjNDNhMT