A team is training a language model to generate helpful and coherent paragraphs. They are comparing two feedback strategies:

• Strategy A: An automated system provides a small reward after every 5 words are generated, based on whether those words match a predefined vocabulary list.
• Strategy B: A human expert reads the entire completed paragraph and provides a single, holistic quality score.

Based on principles of effective training for complex language tasks, which strategy is likely to produce a better model, and why?

Evaluating Feedback Mechanisms for AI Training

In the context of training a large language model to generate creative stories, increasing the frequency of automated reward signals (e.g., a reward for every grammatically correct sentence) is always more effective than providing a single, holistic quality rating from a human expert at the end of the entire story.

Optimizing Feedback for an Empathetic AI