LLM Safety Alignment: Beyond RLHF

As model capabilities grow, safety alignment becomes increasingly important. This article discusses Constitutional AI, RLAIF, and other new approaches.

Limitations of RLHF

Reinforcement Learning from Human Feedback, while effective, suffers from high feedback costs, annotator bias, and scalability issues.

New Method Exploration

• Constitutional AI: Training models to follow predefined principles without human feedback
• RLAIF: Using AI feedback instead of human feedback for scalable alignment
• DPO: Direct Preference Optimization simplifies the training process
• Self-Play: Models improve safety through playing against themselves

Practical Deployment Experience

At Amazon, we adopt a multi-layer defense strategy: input filtering, model-level safety alignment, and output review combined.

Future Challenges

As models become more capable, ensuring they remain aligned with human values becomes more critical and challenging. We need continuous research and vigilance.

Author: Jie Zhu | Published on 2026-02-14