Reference library
Training Compute-Optimal Large Language Models
Paper summary
Uses a large sweep of language-model sizes and token budgets to estimate compute-optimal training allocation. Results favor scaling training data alongside parameters and demonstrate a smaller, more thoroughly trained model under a matched compute budget.
Relevance to the project
Supports fair resource-budget comparisons and qualifies earlier scaling-law recommendations. Measure both network size and lifetime or evolutionary exposure in future experiments.
Compute-optimal language-model training does not establish simulation scaling laws.
Source record
- Reference ID
- chinchilla
- Archived version
- 2203.15556v1
- Summary provenance
- reference-library/papers/chinchilla/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.