DataWorkLearn: The Workspace Is the Training Ground for Data Agents

Data agents, LLM agents that query, transform, and maintain data on a user's behalf, work inside workspaces of interdependent sources: databases and files, documentation, and layered pipelines whose definitions depend on one another, so correctness depends on how these pieces fit together. We introduce DataWorkLearn, which lets an agent learn a workspace before the first question arrives, from only the workspace and a short description of the expected work, with no labeled tasks, hand-written verifiers, or human in the loop. The agent explores the workspace, writes tasks together with executable solutions over its sources and pipelines, and verifies every solution by execution rather than by a model's judgment. Because every task is an instruction, a starting environment, and a verifier, we use the same tasks to adapt the agent through reinforcement learning on its weights, through learned skills, and through harness optimization, and compare the three on identical tasks. Across four data-analysis and data-engineering benchmarks, DataWorkLearn improves a 27B open-weight model by up to 22.5 points on the Data Agent Benchmark (DAB) and 22.4 on DABstep-hard, outperforming GEPA, EvoSkill, and Meta-Harness, each adapted on the same generated tasks, by up to 18.8 points. A data workspace can thus serve as verified, label-free training signal before any question is asked, not only as context at inference time.