A development team is training a large language model to be a helpful assistant. Their process involves two stages:

1. They train a 'scoring model' on a dataset of human-ranked conversations. The goal of this scoring model is to predict which of two responses a human would prefer, assigning a numerical score.
2. They then use this scoring model to automatically provide feedback to the main language model, rewarding it for generating responses that receive a high score.

After extensive training

The Role of the Reward Model in Scalable Training

In the process of fine-tuning a language model using feedback, the problem is often framed using concepts from a general learning paradigm. Match each component from this general paradigm to its specific implementation in the language model fine-tuning process.