Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas
Generally Intelligent
Embrace Accumulation to Overcome Model Collapse
Model collapse is a significant concern in the realm of deep generative models, as evidenced by its occurrence even in linear regression settings. Traditional studies on this phenomenon often operate under the assumption of replacing data with each model iteration, which simplifies the method but overlooks potential improvements. By shifting the focus to accumulating data across iterations—where new models are trained using all available data rather than just previous outputs—there exists the potential to mitigate inherent issues associated with model collapse. This practical adjustment can lead to a deeper understanding and more robust generative modeling techniques, affirming that the approach to data management plays a critical role in model performance.
Remember Everything You Learn from Podcasts
