Synthesizing Occupational Tasks at Scale for Productivity Agents

Synthesizing training data for productivity agents requires more than generating task instructions. The tasks must reflect the work people actually perform in their occupations, and their input materials must provide the information needed to meet the task requirements. We introduce SynthWorker to synthesize occupational tasks and their input materials at scale. SynthWorker leverages news articles relevant to each occupation and sector to provide work context for task generation. Within this context, we use a task hierarchy as the unit of synthesis, constructing shared input materials through web retrieval and synthesis and refining task instructions based on their contents. During execution, intermediate deliverables become additional input materials for dependent tasks, reflecting how real work builds on earlier results. Sharing the materials reduces average input and output token use per task by $6.7\times$ and $9.5\times$ compared with constructing materials separately for each task. We synthesize 100K tasks across 44 occupations in nine sectors and use a teacher model to solve them in dependency order. Fine-tuning the 4B and 9B Qwen3.5 base models on the resulting trajectories yields SynthWorker-4B and SynthWorker-9B, improving GDPval win rates by 39.9 and 44.0 percentage points over their respective base models. These gains extend to the subset of APEX-Agents requiring file deliverables, where mean scores increase by 17.6 and 20.9 percentage points.