SoftwareGym: Scaling Computer-Use Agent Training with Diverse Synthesized Software Environments
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces a scalable, automated pipeline for synthesizing diverse and functionally reliable GUI software environments, demonstrating that training on these synthesized trajectories significantly improves the out-of-domain generalization of compact computer-use agents.
Methodology
The paper proposes SoftwareGym, an automated multi-agent pipeline for synthesizing GUI software environments. The methodology is structured into three stages: Design (generating diverse functional specs and UI blueprints using layout-archetype distance and tree-edit distance to ensure structural diversity), Implementation (coding Avalonia applications with source-level verification), and Evaluation (GUI-based testing with iterative repair). The use of a "runtime-verified" approach where agents interact with the generated software to verify functionality before it enters the training pool is a strong technical contribution. The diversity metrics (Hamming distance for layout, Tree-Edit Distance for structure) are well-defined and effectively address the common issue of LLM-generated UIs converging to similar patterns.
Results & evaluation
The experiments are extensive. The authors synthesize 182 applications across 12 categories. They demonstrate that training a 9B model (Qwen3.5-9B) solely on these synthesized trajectories improves Pass@3 on the out-of-domain OSWorld benchmark from 41.39% to 56.97%. This is a significant improvement for a compact model, outperforming larger specialized agents like EvoCUA-32B and OpenCUA-32B. The ablation studies convincingly show the contribution of structured style design and GUI-guided repair. The creation of SoftwareGym-Bench (120 tasks) provides a dedicated evaluation suite, though its size is relatively small compared to OSWorld.
Reproducibility
The paper provides code and model links. The pipeline relies on specific LLMs (GPT-5.5, Seed 2.1 Pro) for synthesis and evaluation, which may limit reproducibility for groups without access to these specific frontier models or APIs. However, the detailed description of the verification metrics and the release of the 182 applications significantly enhance reproducibility of the dataset and benchmark.
Limitations
The synthesized applications are based on Avalonia (a cross-platform .NET UI framework), which may not fully represent the diversity of native desktop environments (e.g., Windows WPF, macOS SwiftUI, Linux GTK). The 40.0% score on SoftwareGym-Bench, while competitive, is lower than the top API-based agents (GPT-5.5 at 73.3%), suggesting that while the training data is effective for transfer, the 9B model still lags behind frontier API models on complex tasks. The cost of synthesis ($70.76 per app) is non-trivial, though lower than manual development.
Broader impact
This work addresses a critical bottleneck in Computer-Use Agent (CUA) research: the scarcity of diverse, executable software environments for training. By demonstrating that synthesized, verified environments can provide transferable supervision, this paper opens a path toward scaling CUA training without relying solely on manual data collection or limited browser-based environments. It is likely to be widely adopted as a data source or a methodological template for environment synthesis in the agent community. The paper introduces a scalable, automated pipeline for synthesizing diverse and functionally reliable GUI software environments, demonstrating that training on these synthesized trajectories significantly improves the out-of-domain generalization of compact computer-use agents.
Author of this paper? Send a correction.