Part 3/8:
The core of this approach involves reward models that evaluate human-annotated reasoning steps. These models assign positive rewards to correct steps—like correctly adding two equations or solving for a variable—and negative rewards for incorrect ones. Training involves a dataset of math problems annotated by humans, where each step is labeled as correct or incorrect.
Here's a simplified process:
Data Preparation: A set of math problems with annotated solutions, marking each step as correct or incorrect.
Reward Model Training: Using the annotations to train an AI that can evaluate the correctness of each reasoning step.
Model Application: When the AI, such as ChatGPT Math, tries to solve a new problem, it generates step-by-step solutions.