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SoftwareGym addresses the challenge of scaling computer-use agent (CUA) training by automating the synthesis of diverse, reliable GUI software environments, overcoming limitations of native and browser-hosted setups. This pipeline generates structurally varied applications with verified functionality, enabling the creation of a large-scale benchmark and training data. Training CUAs on data from SoftwareGym significantly improves out-of-domain performance on OSWorld and achieves strong results on the synthesized benchmark itself.
Synthesized software environments unlock a 50% boost in CUA performance on unseen benchmarks, proving scalable supervision is key.
Computer-use agents (CUAs) are rapidly improving, yet synthesizing diverse and reliable software environments at scale remains challenging. Native application environments require substantial construction and integration effort, which limits application coverage and increases the risk of application-specific overfitting. Browser-hosted environments provide limited interaction depth and workflow complexity. These limitations motivate SoftwareGym, an automated pipeline for synthesizing GUI software environments with diverse interfaces, executable functionality, and runtime-verified behavior. It generates structurally diverse designs, realizes them as runnable applications, and uses GUI evaluation with iterative repair to ensure functional reliability. Using SoftwareGym, we synthesize 182 applications across twelve categories and achieve a mean GUI task pass rate of 91.5%. We construct SoftwareGym-Bench, a 120-task evaluation suite spanning the same twelve categories, and use trajectories collected from these environments for CUA training. The resulting model improves Pass@3 from 41.39% to 56.97% on the strictly out-of-domain OSWorld benchmark and achieves 40.0% on SoftwareGym-Bench. These results demonstrate the potential of synthesized software environments as scalable and transferable supervision for CUA training.
