Understanding RLHF

Reinforcement Learning from Human Feedback (RLHF) is a technique for aligning language models with human preferences. Rather than relying solely on next-token prediction, RLHF uses human judgment to guide model behavior toward helpful, harmless, and honest outputs.

Table of Contents

• Core Concepts
• The RLHF Pipeline
• Preference Data
• Instruction Tuning
• Reward Modeling
• Policy Optimization
• Direct Alignment Algorithms
• Challenges
• Best Practices
• References

Core Concepts