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arXiv · 2610.21656

Data World: Scaling Data Environments from Scratch

Zhanli Li·Zeyuan Xu·Chaoxu Pang·Zeyuan Liu·Anyang Ji·Te Sun·Kongcheng Zhang·Wenbo Xu·Xubin Yue·Shipeng Luo·Junjie Zhao·Yingfan Hua·Jingqing Ruan·Zhongda Su·Feng Hong·Zhizhao Zeng·Ke Zeng·Xunliang Cai·Hanjia Ye
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Abstract

Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property restrictions, and difficulty verifying open-ended findings without known generative truth. Our core insight is to specify the world behind the files before generating the files themselves. We introduce Data World, a multi-agent engine that synthesizes environments, tasks, and rubrics from explicit mechanisms. Independent audits check target recoverability from observed files; rubrics support evaluation and RL rewards. Human preference-based curation for question realism selects 100 tasks across 50 held-out worlds and 16 domains for DataWorld-Bench. The engine also yields 2,135 training tasks from 1,037 disjoint worlds. Evaluation of ten frontier models shows that the benchmark remains challenging, particularly for insight discovery, with a consistent computation-insight gap. RL-zero training on the synthesized tasks produces DataWorld-35B-A3B from Qwen3.6-35B-A3B without trajectory distillation. Under matched evaluation, it improves over its base by 7.06 points on DataWorld-Bench, 14.28 on DAComp, 20.90 on DABstep-Hard, and 12.13 on JobBench, a broader occupational benchmark.

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Zhanli Li, Zeyuan Xu, Chaoxu Pang, Zeyuan Liu, Anyang Ji, Te Sun, Kongcheng Zhang, Wenbo Xu, Xubin Yue, Shipeng Luo, Junjie Zhao, Yingfan Hua, Jingqing Ruan, Zhongda Su, Feng Hong, Zhizhao Zeng, Ke Zeng, Xunliang Cai, Hanjia Ye. 2026-10-15. Data World: Scaling Data Environments from Scratch. https://arxiv.org/abs/2610.21656

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