Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification
Score
79提出了新的交互式验证框架和专用基准，显著推进了GUI任务自动评估方法。
方法描述清晰，实验设计合理，结果统计详实，但缺乏与更多基线对比。
可用于实际GUI代理的训练和测试阶段缩放，具有较好应用前景。
论文刚发布，内容紧跟GUI代理评估这一前沿热点。
来源为arXiv预印本，作者来自知名机构但尚未正式发表。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
Novel approach, thorough experiments, significant results, important for GUI agent evaluation and training.
AI commentary
「This paper addresses the pain point of environment state acquisition in GUI evaluation, and the proposed IRA method is both innovative and practical. For researchers and engineers working on GUI automation and AI agent training, IRA not only provides a reliable reward signal but also inspires how to combine system tools with interface information for verification. It's worth watching its scalability to more complex cross-application scenarios.」
Quick overview
This paper proposes an Interactive Reward Agent (IRA) based on a 'propose-then-verify' framework that acquires and verifies evidence from the post-execution environment by invoking system tools, application tools, and GUI tools to determine task completion. It also introduces GUI-RewardBench, a benchmark of 321 GUI task trajectories across 10 Ubuntu desktop applications. IRA achieves 86.9% accuracy on this benchmark and is applied to reinforcement learning for GUI agents, achieving a 34.0% OSWorld success rate.
- Proposes IRA framework that addresses GUI task evaluation challenges via interactive environment-state verification
- Introduces GUI-RewardBench benchmark covering 321 task trajectories across 10 Ubuntu application categories
- IRA achieves 86.9% accuracy, outperforming existing evaluator baselines
- IRA provides effective reward signals for training GUI agents, boosting OSWorld success rate to 34.0%
Full text
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