代理奖励建模：通过在线主动交互验证GUI代理
Agentic Reward Modeling: Verifying GUI Agent via Progressive Trajectory-Grounded Interaction
AI总结：
VAGEN通过引入代理交互验证范式，解决GUI代理评估中的视觉限制问题，提升评估准确性与性能。
摘要：
可验证的强化学习（RLVR）对于GUI代理的持续进化至关重要，但现有的评估范式面临显著限制。基于规则的方法存在可扩展性差的问题，无法处理开放性任务，而LLM-as-a-Judge方法依赖于被动的视觉观察，往往无法捕捉到潜在的系统状态，因为存在部分状态可观测性。为了解决这些挑战，我们倡导从被动评估向代理交互验证的范式转变。我们引入了VAGEN框架，该框架采用配备交互工具的验证者代理，能够自主规划验证策略并主动探测环境以寻找任务完成的证据。利用GUI任务通常是'易于验证但难以解决'的洞察，VAGEN克服了视觉限制的瓶颈。在OSWorld-Verified和AndroidWorld基准测试中，实验结果表明，VAGEN在对比LLM-as-a-Judge基线方法时显著提高了评估准确性，并通过测试时扩展策略进一步提升了性能。
英文摘要：
Reinforcement learning with verifiable rewards (RLVR) provides a promising pathway for continuously advancing GUI agents, yet existing reward modeling paradigms face complementary limitations. Rule-based methods suffer from poor scalability and cannot handle open-ended tasks. LLM-as-a-Judge methods enable scalable trajectory verification but remain passive and are constrained by partial state observability, since key evidence often resides in latent environment states beyond the trajectory. Recent active environment interaction methods mitigate observability issues but tend to over-rely on probing while under-utilizing direct trajectory evidence, leading to verification inefficiency. To address these challenges, we advocate a trajectory-grounded interactive verification paradigm. We introduce VAGEN, a framework that employs a tool-augmented verifier agent governed by a Progressive Verification Mechanism, which follows a surface-to-latent and cheap-to-expensive design philosophy to extract trajectory evidence and probe environment states in a proactive end-to-end manner. Experiments on OSWorld-Verified and AndroidWorld benchmarks demonstrate that VAGEN significantly improves evaluation accuracy with a favorable performance-efficiency trade-off.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
