A framework for improving model alignment without expensive manual annotations using dual learning and self-supervised feedback.
1. **Scalability**: Training can scale with compute rather than human labor.
2. **Consistency**: Self-supervised signals are deterministic and free from human inter-rater variability.
3. **Domain Adaptation**: Models can be aligned in specialized domains (e.g., coding, law) where finding qualified human annotators is difficult.