A framework for improving model alignment without expensive manual annotations using dual learning and self-supervised feedback.
Aligning Large Language Models (LLMs) with human intent has traditionally relied heavily on Reinforcement Learning from Human Feedback (RLHF). While effective, RLHF is bottlenecked by the need for extensive, high-quality human annotations. Self-Supervised Preference Optimization represents a paradigm shift, introducing frameworks that allow models to improve their alignment through self-generated feedback loops, significantly reducing the reliance on manual labeling.