Scaling laws help predict model performance and guide resource allocation in AI development.
Key Findings
- Loss decreases predictably with scale
- Different capabilities emerge at different scales
- Compute-optimal training ratios exist
Implications
- Larger models generally better
- But diminishing returns
- Efficiency innovations valuable