Scaling Laws for Neural Language Models.¶
Kaplan, J., McCandlish, S., Henighan, T., & Brown, T. B. (2020). Scaling Laws for Neural Language Models.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Aggregate-Marginal Divergence
- AI compute and conservation. Aggregate model performance improves while marginal benefit per training unit collapses along the scaling curve, and protected-area acreage grows while each added hectare is more remote and less biodiverse.
This sourceDocuments diminishing marginal returns to compute/data/parameters along power-law scaling curves while aggregate capability keeps rising — the forced aggregate-marginal divergence.
- AI compute and conservation. Aggregate model performance improves while marginal benefit per training unit collapses along the scaling curve, and protected-area acreage grows while each added hectare is more remote and less biodiverse.
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Registry ID ref:e63f05d88926 · see in the full table