Part 9/11:
- Multi-Turn Reinforcement Feedback: The model then self-evaluates and refines its answers across multiple rounds, with the reward system emphasizing overall accuracy rather than merely superficial adjustments.
A key aspect is reward shaping, which guides the model to prioritize correcting core issues. This incentivizes larger, more impactful edits and discourages over-conservative changes—leading to more effective self-improvement.
Broader Implications and Future Directions
Score could fundamentally change how AI systems self-improve, moving from static, externally tuned models to dynamic, self-correcting entities. Its potential applications are vast:
- Enhanced Code Generation: More reliable AI code assistants and developers.