Part 7/11:
For example, efficiency in self-correcting enables models to better handle real-world scenarios where errors are inevitable, making them more dependable in critical areas—such as automated research, financial modeling, or advanced tutoring systems.
Why Traditional Approaches Fall Short
Prior methods—like supervised fine-tuning or running a secondary model as a verifier—have critical limitations. Supervised fine-tuning often amplifies existing biases, leading to shallow corrections that don’t address the root problem. External verification is resource-heavy and hard to scale.