Dataset quality vs architecture: Where's the real ML bottleneck?
Scaling existing architectures vs curating data—which yields bigger gains?
Deep Dive
A recent Reddit post asks whether ML progress is bottlenecked by dataset quality or model architecture. The user notes that recent gains largely come from scaling existing architectures, while emphasis grows on data curation and synthetic data. In applied settings, data constraints often become limiting before architecture does, but it's unclear if this holds across all domains.
Key Points
- Recent ML gains come from scaling existing architectures, not inventing new ones
- Data quality and synthetic data pipelines are increasingly seen as the bigger lever
- In applied settings, data constraints often limit performance before architecture does
Why It Matters
Resource allocation: teams must decide between data curation and model design for bigger returns.