RLHF

AI Reasoning Logo RLHF Book by Nathan Lambert 2026

FOUNDATION LAYER # 

┌─────────────────────────────────────────────────────────────┐
│ 1. Overview         2. History        3. Training Overview  │
│ [What & Why]       [Evolution]        [Problem Setup]       │
└─────────────────────────────────────────────────────────────┘

CORE METHODOLOGY LAYER # 

┌─────────────────────────────────────────────────────────────┐
│  4. Instruction Fine-tuning  [Data Preparation]             │
│  5. Reward Modeling          [Preference Learning]          │
│  6. Reinforcement Learning   [Core Optimization]            │
└─────────────────────────────────────────────────────────────┘

ADVANCED TECHNIQUES LAYER # 

┌─────────────────────────────────────────────────────────────┐
│  7. Reasoning & Inference-Time Scaling  [2024-25 Frontier]  │
│  8. Direct Alignment Algorithms         [DPO & Variants]    │
│  9. Rejection Sampling                  [Simple RL Alt]     │
└─────────────────────────────────────────────────────────────┘

DATA & THEORY LAYER # 

┌─────────────────────────────────────────────────────────────┐
│ 10. Nature of Preferences  [Philosophy]                     │
│ 11. Preference Data        [Collection & Design]            │
│ 12. Synthetic Data         [AI Feedback & CAI]              │
└─────────────────────────────────────────────────────────────┘

APPLICATION & CONTROL LAYER # 

┌─────────────────────────────────────────────────────────────┐
│ 13. Tool Use & Function Calling  [Agentic Capabilities]     │
│ 14. Style and Information        [Format & Tone]            │
│ 15. Regularization              [Stability Controls]        │
│ 16. Over-Optimization           [Failure Modes]             │
└─────────────────────────────────────────────────────────────┘

EVALUATION & DEPLOYMENT LAYER # 

┌─────────────────────────────────────────────────────────────┐
│ 17. Evaluation                   [Measurement]              │
│ 18. Product, UX, Model Character [Real-World Deploy]        │
└─────────────────────────────────────────────────────────────┘

REFERENCE # 

┌─────────────────────────────────────────────────────────────┐
│ Appendix A: Definitions  [Technical Reference]              │
└─────────────────────────────────────────────────────────────┘

Foundation Layer #

  • Chapter 1: Overview - What RLHF is and why it matters
  • Chapter 2: History - Evolution of the field
  • Chapter 3: Training Overview - Problem setup and fundamentals

Core Methodology Layer #

  • Chapter 4: Instruction Fine-tuning - Data preparation techniques
  • Chapter 5: Reward Modeling - Learning from preferences
  • Chapter 6: Reinforcement Learning - Core optimization algorithms

Advanced Techniques Layer #

  • Chapter 7: Reasoning & Inference-Time Scaling - 2024-25 frontier methods
  • Chapter 8: Direct Alignment Algorithms - DPO and variants
  • Chapter 9: Rejection Sampling - Simple RL alternatives

Data & Theory Layer #

  • Chapter 10: Nature of Preferences - Philosophical foundations
  • Chapter 11: Preference Data - Collection and design strategies
  • Chapter 12: Synthetic Data - AI feedback and Constitutional AI

Application & Control Layer #

  • Chapter 13: Tool Use & Function Calling - Agentic capabilities
  • Chapter 14: Style and Information - Format and tone control
  • Chapter 15: Regularization - Stability controls
  • Chapter 16: Over-Optimization - Failure modes and mitigation

Evaluation & Deployment Layer #

  • Chapter 17: Evaluation - Measurement techniques
  • Chapter 18: Product, UX, Model Character - Real-world deployment

Reference #

  • Appendix A: Definitions - Technical reference guide