Data Darwinism Part II: DataEvolve -- AI can Autonomously Evolve Pretraining Data Curation
Data Darwinism (Part I) established a ten-level hierarchy for data processing, showing that stronger processing can unlock greater data value. However, that work relied on manually designed strategies for a single category. Modern pretraining corpora comprise hundreds of heterogeneous categories spanning domains and content types, each demanding specialized treatment. At this scale, manual strategy design becomes prohibitive. This raises a key question: can strategies evolve in an automated way? We introduce DataEvolve, a framework that enables strategies to evolve through iterative optimization rather than manual design. For each data category, DataEvolve operates in a closed evolutionar...
| Authors | Tiantian Mi, Dongming Shan, Zhen Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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