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New CD scaling laws address compute-data imbalance in AI pretraining

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Compute-Data (CD) scaling laws, a new framework that addresses the growing imbalance between compute availability and high-quality pretraining data. This framework extends existing scaling laws by incorporating a token-effectiveness function that quantifies the value of data derived from methods like multi-epoch repetition or paraphrasing. The study found that token effectiveness is not constant but varies with model size, data expansion strategies, and the amount of derived data, suggesting diminishing returns when substituting compute for data. AI

IMPACT This research could lead to more efficient AI model pretraining by optimizing the use of available data and compute resources.

RANK_REASON The cluster contains an academic paper detailing new research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CD scaling laws address compute-data imbalance in AI pretraining

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The cluster contains an academic paper detailing new research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Bridging Compute- and Data-Optimal Pretraining

    Classical compute-optimal scaling laws assume an unbounded supply of fresh pretraining data, yet pretraining is increasingly entering a regime in which compute grows faster than the availability of high-quality data. We propose Compute-Data (CD) scaling laws, a unified framework …

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