About This Project
Making complex AI alignment concepts accessible through intuitive visualizations, interactive playgrounds, and educational storytelling.
Project Overview
RLHF Illustrated Guide is a comprehensive, interactive web platform that transforms the complex mathematics and concepts behind Reinforcement Learning from Human Feedback (RLHF) into an engaging, visual learning experience.
RLHF has become the cornerstone of modern AI alignment—it's how ChatGPT, Claude, and other large language models learn to be helpful, harmless, and honest. Yet, most resources on RLHF are either too academic (dense mathematical papers) or too shallow (oversimplified blog posts).
This guide fills that gap by providing rigorous yet accessible education with hands-on interactive elements that build true understanding.
Key Features
Interactive Visualizations
30+ D3.js-powered charts and simulations. Adjust parameters in real-time and see how algorithms behave. Export as PNG/SVG for presentations.
12 Complete Modules
From RLHF basics to Constitutional AI. Each module follows a proven template: equation, intuition, analogy, visualization, and assessment.
Concept Playground
Experiment with PPO, DPO, and rejection sampling algorithms. Compare methods side-by-side with consistent parameters.
Intuitive Analogies
Complex concepts made concrete through carefully crafted analogies: Game Bot for RL, Writing Student for preferences, Math Tutor for reasoning.
Assessment & Feedback
Each module includes 5-7 quiz questions with instant feedback and detailed explanations to reinforce key concepts.
Production Ready
Built with accessibility (WCAG 2.1 AA), responsive design, dark mode, and performance optimization in mind.
The Analogy Toolbox
Complex concepts become intuitive through carefully crafted analogies that carry through the entire curriculum:
Atari Game Bot
For core RL concepts: policy as game strategy, rewards as score points, value functions as game state evaluation.
Creative Writing Student
For preference learning: reward model as editor's taste, preference data as manuscript feedback, policy optimization as iterative revision.
Math Tutor Bot
For reasoning and verification: verifiable rewards as correct answers, chain-of-thought as showing work, tool use as using a calculator.
Advanced Concepts
For constitutional AI and evaluation: AI constitutions as ethical guidelines, self-critique as peer review, over-optimization as teaching to the test.
Curriculum Overview
Phase 1: Core RLHF Loop
Phase 2: Foundation & Practice
Phase 3: Advanced Topics
Technology Stack
Frontend & Framework
Visualization & Content
Infrastructure & Deployment
Technical Highlights
Server-Side Rendering
MDX content rendered server-side for SEO. D3 visualizations hydrated on client for interactivity. Dynamic imports for optimal code splitting.
Performance Optimized
Code splitting for D3, Framer Motion, and vendor bundles. Image optimization with AVIF/WebP. Target: LCP <2.5s, FID <100ms, CLS <0.1.
Accessibility First
WCAG 2.1 AA compliant. ARIA labels for visualizations. Full keyboard navigation. High contrast color ratios. Screen reader compatible.
Type-Safe Architecture
TypeScript strict mode throughout. Explicit return types. No any types. Comprehensive interfaces for all components and data structures.
Who This Is For
ML Practitioners
Quickly understand RLHF to implement in production systems
Students
Visual learning complements academic ML/AI courses
Researchers
Intuitive grounding before diving into academic papers
Educators
Ready-made visualizations and materials for teaching
Credits & Acknowledgments
This project is inspired by and based on concepts from Nathan Lambert's RLHF book, which provides an excellent comprehensive treatment of RLHF from first principles.
The interactive approach draws inspiration from projects like Distill.pub and 3Blue1Brown, which have shown how powerful visual explanations can be for complex mathematical concepts.
Ready to Learn RLHF?
Start with the introduction module or jump into the interactive playground.