Part 3/11:
Introducing Score: An Autonomous Self-Correction Technique
DeepMind’s score offers a fresh approach rooted in reinforcement learning. Unlike earlier methods that relied heavily on supervised training or external verification systems, score teaches models to learn from their own errors and iteratively improve their responses.
Key Innovations
Elimination of Supervised Fine-Tuning: Traditional methods depend on large datasets of errors and corrections, which tend to embed existing biases and limit flexibility. In contrast, score allows models to generate and learn from their own correction data.
Two-Stage Training Process: