RLHF Illustrated Guide

An interactive web platform that transforms complex Reinforcement Learning from Human Feedback (RLHF) concepts into engaging visual learning experiences.

Overview

This comprehensive educational platform provides:

• Rigorous yet accessible education on AI alignment
• Hands-on interactive elements with real-time visualizations
• 12 complete modules covering RLHF fundamentals to Constitutional AI
• Multiple learning lenses: mathematical, visual, and operational

Key Features

• 30+ Interactive D3.js Visualizations: Adjust parameters in real-time and see how algorithms behave
• 4 Analogy Types: Game Bot, Writing Student, Math Tutor, and Advanced Concepts to make complex ideas concrete
• Assessment System: 60+ quiz questions with detailed explanations to reinforce key concepts
• Production Quality: Server-side rendering, performance optimized, type-safe architecture

The Analogy Toolbox

Complex concepts become intuitive through carefully crafted analogies:

• 🎮 Atari Game Bot: For core RL concepts like policy as game strategy and rewards as score points
• ✍️ Creative Writing Student: For preference learning where the reward model acts as an editor's taste
• 🧮 Math Tutor Bot: For reasoning and verification with verifiable rewards
• 🧠 Advanced Concepts: For constitutional AI and evaluation frameworks

Curriculum Highlights

Phase 1: Core RLHF Loop

• Introduction to RLHF and the four-stage pipeline
• Reward Modeling with Bradley-Terry and pairwise preferences
• Policy Gradients (PPO) with trust regions and clipping
• Direct Preference Optimization for offline alignment

Phase 2: Foundation & Practice

• Problem setup and mathematical definitions
• Instruction tuning with chat templates
• Regularization techniques including KL penalties
• Rejection sampling and Best-of-N methods

Phase 3: Advanced Topics

• Constitutional AI with principles and self-improvement
• Reasoning training with RLVR and chain-of-thought
• Tool use and function calling with MCP architecture
• Synthetic data, evaluation, and over-optimization

Visit the full interactive platform to start learning RLHF.