Data World: A Multi-Agent Engine That Synthesizes Data Environments From Scratch
Data World is a multi-agent engine whose core idea is to specify the "world" behind a set of files, via explicit mechanisms, before generating the files themselves — synthesizing environments, tasks and rubrics for data agents. The authors build DataWorld-Bench through human preference-based curation, selecting 100 tasks across 50 held-out worlds and 16 domains, and generate 2,135 training tasks from 1,037 disjoint worlds; evaluation of ten frontier models shows the benchmark remains challenging, especially for insight discovery, with a consistent computation-insight gap. RL-zero training on those tasks produces DataWorld-35B-A3B from Qwen3.6-35B-A3B without trajectory distillation, which the paper reports 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.
Curating verifiable training environments is a bottleneck for data agents, so an open recipe for synthesizing them is useful — though the reported gains still need independent replication.