CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements
Abstract
Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllability, and reliability of interaction data. Moreover, conventional optimization pipelines lack iterative feedback, while existing recursive improvement approaches often rely on self-generated experiences, limiting ability expansion and yielding diminishing returns. To address these challenges, we introduce CUA-Master, a nested double-loop recursive improvement framework. At the system level, an outer world-data-model flywheel continuously incorporates open-world corpus and leverages evolving model weaknesses to guide subsequent data construction and optimization iterations. At the data level, an inner flywheel couples open-world seeding and autocatalytic propagation. The former mines authentic workflows as seed tasks from instructional videos, and employs visual-semantic dual-path trajectory reconstruction; The latter transforms these seeds into reusable primitives for controllable synthesis of diverse, complex, and verifiable tasks. To enable efficient and reliable trajectory collection, we further introduce a feedback-guided snapshot-rollback retry mechanism in various real environments. Extensive experiments demonstrate that CUA-Master-27B achieves competitive performance across GUI-only and GUI-CLI hybrid benchmarks, scoring 80.2 on OSWorld Verified, 36.9 on OSWorld 2.0, 43.9 on WeaveBench, and 25.0 on Agents' Last Exam. Our framework also substantially improves the quality and efficiency of data synthesis, and exhibits continuous gains across recursive iterations.
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
CUA-Master introduces a nested double-loop recursive framework that significantly advances Computer-Use Agents by integrating open-world data seeding with autocatalytic propagation and feedback-guided rollout retries, achieving competitive performance on major benchmarks with a 27B parameter model.
Methodology
The paper proposes CUA-Master, a nested double-loop recursive improvement framework for Computer-Use Agents (CUAs). The core innovation lies in the "World-Data-Model" flywheel, which integrates an outer loop for system-level optimization (guiding data construction based on model weaknesses) and an inner "Data Flywheel" for scalable synthesis. The inner flywheel combines "Open-World Seeding" (mining tasks from instructional videos via visual-semantic dual-path reconstruction) with "Autocatalytic Propagation" (abstracting tasks into reusable primitives for compositional synthesis). A key technical contribution is the "feedback-guided snapshot-rollback retry mechanism," which improves rollout efficiency by preserving valid execution prefixes and recovering from localized failures. The methodology is well-structured, addressing the common pitfalls of closed-world self-improvement (diminishing returns) and unidirectional data pipelines (lack of feedback). The use of a hybrid GUI+CLI action space is also a significant practical contribution.
Results & evaluation
The experiments are extensive, evaluating CUA-Master-27B on four major benchmarks: OSWorld Verified, OSWorld 2.0, WeaveBench, and Agents' Last Exam. The model achieves competitive performance, scoring 80.2 on OSWorld Verified and 25.0 on Agents' Last Exam, surpassing many larger open-source models and approaching top closed-source models like GPT-5.6-Sol and Claude-Opus-5. The paper provides detailed ablations on the effectiveness of the hybrid action space, the data flywheel's impact on diversity and rollout success rates, and the progressive gains from recursive training iterations. The inclusion of cross-platform generalization tests (Windows, macOS) further strengthens the evaluation. The results demonstrate that the proposed framework effectively scales data quality and model capability.
Reproducibility
The paper provides sufficient detail on the training recipe (SFT followed by RL), hyperparameters, and infrastructure setup (VM pool, resource scheduling). However, the specific prompts for the "annotator" model roles and the exact criteria for primitive extraction are referenced in figures/appendices that are not fully detailed in the text provided. The reliance on a powerful external annotator model for data generation introduces a dependency that may limit reproducibility for groups without access to such models. The code and data are not explicitly linked in the provided text, though the methodology is described in enough detail for a skilled team to attempt replication.
Limitations
The primary limitation is the high computational and infrastructure cost, particularly for the large-scale VM deployment and iterative training. The framework's performance is heavily dependent on the quality of the external annotator model, which introduces additional inference costs and potential biases. The paper acknowledges that the upper limit of synthesized data quality is bounded by the annotator's capabilities. Additionally, the recursive nature of the pipeline makes it complex to manage and debug compared to simpler training paradigms.
Broader impact
This work has significant implications for the development of autonomous agents capable of interacting with complex software environments. By providing a scalable and reliable method for generating high-quality training data from open-world sources, it addresses a critical bottleneck in CUA research. The hybrid GUI+CLI approach and the snapshot-rollback mechanism are likely to be adopted by other groups working on agent-based systems. The framework's ability to continuously improve through recursive data-model feedback loops offers a promising path toward more general and capable AI agents. CUA-Master introduces a nested double-loop recursive framework that significantly advances Computer-Use Agents by integrating open-world data seeding with autocatalytic propagation and feedback-guided rollout retries, achieving competitive performance on major benchmarks with a 27B parameter model.
Author of this paper? Send a correction.