Scaling Laws & Stability
Scaling Laws & Stability questions from AI Research Scientist interviews. Work through the answer, then check yourself — each takes a few minutes.
- Depth vs width tradeoffs in transformer scaling
- Emergent capabilities and scaling discontinuities
- Implicit regularization of SGD
- Loss landscape geometry
- Neural scaling law breakdowns and irreducible loss
- Numerical precision and mixed-precision failure modes
- Scaling laws and compute-optimal allocation
- Sharpness and flat minima
- Sharpness-aware minimization (SAM) algorithm
- Training instability diagnosis
One sitting · 20–30 minutes
A focused session on Scaling Laws & Stability for your AI Research Scientist interview
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