Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models

Understand

The key to effective alignment lies in high-quality preference data.

• Recent research has focused on automated alignment, which involves developing alignment systems with minimal human intervention.
• However, prior research has predominantly focused on developing data generation methods, while insufficient attention has been paid to quality control mechanisms, which often produce inaccurate and unhelpful data, leading to unpredictable benefits during iterative optimization.
• In this paper, we present Self-Steering Optimization ($SSO$), an algorithm that autonomously generates high-quality preference data, eliminating manual annotation requirements.