A machine learning team is using a step-by-step feedback process to improve a model that generates multi-step solutions. They have a limited budget for human annotation and are considering two strategies for selecting which reasoning steps to review:

Strategy 1: Prioritize annotating steps where the model has a very low confidence score, indicating it is uncertain about its own reasoning.

Strategy 2: Prioritize annotating steps where the model has a very high confidence score, but the step is factually incorrect.

Evaluate these two strategies. In your response, argue which strategy is likely to lead to more significant and efficient improvements in the model's overall performance, and explain the underlying reasoning for your choice.