Part 4/11:
Correction Strategy Learning: The first stage instructs the model to produce meaningful corrections without getting caught in minor tweaks that do not significantly improve the response. This helps the model develop a robust correction approach.
Reinforcement Learning with Multi-Turn Feedback: In the second stage, the model is rewarded for making substantial, accurate corrections across multiple attempts, gradually improving its ability to refine responses effectively.
- Reward Shaping: This critical component guides the model towards making meaningful adjustments, discouraging minimal or superficial fixes that don't address the underlying issue.