OpenComputer introduces a verifier-grounded framework with state verifiers, self-evolving layers, task synthesis, and auditable evaluation for 33 desktop apps and 1000 tasks to support computer-use AI agents.
Agentic reward modeling: Verifying gui agent via online proactive interaction.arXiv preprint arXiv:2602.00575
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.
citing papers explorer
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OpenComputer: Verifiable Software Worlds for Computer-Use Agents
OpenComputer introduces a verifier-grounded framework with state verifiers, self-evolving layers, task synthesis, and auditable evaluation for 33 desktop apps and 1000 tasks to support computer-use AI agents.
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GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.