Empirical Alignment Research

Empirical Alignment Research

Hands-on implementation and testing of state-of-the-art alignment techniques.

RLHF (Reinforcement Learning from Human Feedback)

• Supervised Fine-tuning: Initial behavior cloning from demonstrations
• Reward Model Training: Learning human preferences from comparisons
• PPO Optimization: Reinforcement learning against reward model
• KL Penalties: Preventing mode collapse and maintaining diversity

Constitutional AI

• Principle-Based Training: Encoding values as constitutional principles
• Self-Critique: Models evaluate their own outputs
• Revision Training: Learning to improve based on critiques
• Reduced Human Feedback: Scaling oversight through AI assistance

Advanced Techniques

• Direct Preference Optimization (DPO)
• Instruction Following through FLAN/InstructGPT
• Safety-specific fine-tuning approaches
• Multi-objective alignment methods

Experimental Methodology

• Benchmark design and evaluation
• A/B testing alignment techniques
• Red teaming aligned models
• Long-term behavior analysis