StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents
Quick Answer
StainFlow introduces a novel entity-stain tracking model for GUI agents, improving online RL success by 3.2% and trajectory completion judgment accuracy by 1.8% on benchmarks like AndroidWorld and OGRBench.
Quick Take
It addresses limitations in existing Process Reward Models by providing objective task phase separation and dynamic evidence linking.
Key Points
- StainFlow enhances GUI agent training with objective entity tracking.
- Global Entity Stain Tracking reduces subjectivity in task phase separation.
- Local Stain Evidence Linking dynamically constructs evidence windows.
- Achieved 3.2% improvement in online RL success on AndroidWorld.
- Trajectory completion judgment accuracy increased by 1.8% on OGRBench.
Paper Resources
Source Excerpt
arXiv:2606. 07027v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, stochastic digital environments, but trajectory-level success feedback is too sparse to provide reliable credit assignment for intermediate exploration steps. To mitigate this issue, recent studies introduce Process Reward Models (PRMs), which provide finer-grained training feedback through global milestone verification or local step-level evaluation. …
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