In other words, as models train on AI-generated content, they may begin to amplify subtle imperfections in their outputs. These feedback loops can perpetuate and magnify existing biases, creating a compounding effect that becomes increasingly difficult to detect and correct.
OpenAI's Foundations team is developing new filtering mechanisms to maintain data quality, implementing different validation techniques to distinguish between high-quality and potentially problematic synthetic content. The team is also exploring hybrid training approaches that strategically combine human and AI-generated content to maximize the benefits of both sources while minimizing their respective drawbacks.