Editing Scaling Laws (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Scaling Dimensions and Their Effects
! Dimension !! Effect on Loss !! Diminishing Returns !! Practical Limit
|-
| Parameters (N) || Power-law improvement || Yes (α ≈ 0.076) || GPU memory
|-
| Training tokens (D) || Power-law improvement || Yes (β ≈ 0.095) || Data availability / quality
|-
| Training compute (C) || Power-law improvement || Yes (γ ≈ 0.050) || Cost
|-
| Inference compute || Improves reasoning || Yes || Latency budget
|-
| Context length || Enables new tasks || Task-dependent || Quadratic attention cost
|}

'''Failure modes''': Benchmark saturation — as models improve, benchmarks get saturated, making it hard to measure progress. Emergent abilities may not appear for specific domains even at high scale (domain-specific emergence thresholds differ). Data quality is not captured in scaling laws — more low-quality tokens can hurt. Scaling laws derived on language models may not transfer to other modalities.
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