Step-level Optimization for Efficient Computer-use Agents
Researchers propose a step-level optimization framework to improve the efficiency of computer-use agents by reducing reliance on large models during routine GUI interactions. The system uses lightweight monitors to detect high-risk situations—such as stalled progress or semantic drift—and only escalates to more powerful models when necessary. This event-driven cascade approach enables adaptive compute allocation without modifying existing agent architectures or requiring retraining.
- ▪The proposed framework uses a small default policy and escalates to a larger model only when risk is detected.
- ▪Two monitors—Stuck Monitor and Milestone Monitor—are used to detect progress degradation and semantic drift, respectively.
- ▪The approach improves efficiency by replacing constant use of large models with on-demand inference based on interaction dynamics.
- ▪The system is modular and can be integrated with existing computer-use agents without architectural changes or retraining.
- ▪The paper was submitted to arXiv on April 29, 2026, under the subject Artificial Intelligence (cs.AI).
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Computer Science > Artificial Intelligence arXiv:2604.27151 (cs) [Submitted on 29 Apr 2026] Title:Step-level Optimization for Efficient Computer-use Agents Authors:Jinbiao Wei, Kangqi Ni, Yilun Zhao, Guo Gan, Arman Cohan View a PDF of the paper titled Step-level Optimization for Efficient Computer-use Agents, by Jinbiao Wei and 4 other authors View PDF HTML (experimental) Abstract:Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfaces instead of relying on brittle, application-specific integrations.
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