Reference library
Scaling Laws for Neural Language Models
Paper summary
Fits empirical language-model loss trends across parameter count, data size, and training compute. The resulting scaling relations guide allocation of a fixed training budget within the studied regimes.
Relevance to the project
A methodological example of matched-budget comparisons and learning curves. The evolving engine requires its own scaling measurements and objectives.
Empirical laws are regime-dependent and should be compared with later compute-optimal work.
Source record
- Reference ID
- scaling-laws
- Archived version
- 2001.08361v1
- Summary provenance
- reference-library/papers/scaling-laws/metadata.json · summary
- Review status
- initial orientation summary; not a critical full-paper review
- Reading scope
- Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.