A framework for improving model alignment without expensive manual annotations using dual learning and self-supervised feedback.
Self-Supervised Preference Optimization moves us closer to "autonomous alignment," where models can iteratively improve themselves. As these methods mature, we can expect the cost of training high-performing, aligned models to decrease, democratizing access to safe and helpful AI systems.