Problem
Research questions and friction points this paper is trying to address.
GUI reward modeling
outcome reward
task-adaptive
Task-Adaptive Rubrics for GUI Reward Modeling
🤖 AI Summary
为解决GUI奖励模型中任务适应性不足的问题，提出AdaptRubric框架，通过粗到细两个阶段构建更适应具体任务的评判标准。
📝 Abstract
Recent studies on GUI agents have increasingly focused on outcome reward modeling, which assigns outcome rewards by judging whether an executed trajectory satisfies the success criteria implied by the user instruction. Existing GUI reward verifiers, however, often under-specify how these criteria should be constructed for each task instance. Whether using generic rubric structures or implicit model reasoning, their judging criteria are not sufficiently task-adaptive: they can transfer checks across tasks, overlook concrete constraints in the current instruction, or become overly strict by enforcing unstated requirements. To address this limitation, we propose AdaptRubric, a Coarse-to-Fine Rubrics Framework that constructs task-adaptive judging criteria through a category-level coarse stage and an instance-level fine stage. AdaptRubric performs category-level coarse rubric retrieval by routing the instruction to a GUI task family and retrieving reusable task-family criteria, then conducts instance-level fine rubric generation to surface compact cues for concrete values, scopes, and constraints in the current instruction. Across offline reward evaluation and online reinforcement learning optimization, AdaptRubric consistently outperforms prior reward agents, improving F1 by 3.6 points over the baseline average under a matched image budget and yielding a 4.23-point task-success gain.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Task-Adaptive Judging Criteria
Coarse-to-Fine Rubrics Framework
GUI Reward Modeling
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