SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents

GUI agents automate tasks on digital devices by grounding language instructions in visual interfaces. Existing work has improved GUI action prediction through group-relative reinforcement learning, which assigns credit by comparing rewards across sampled responses. However, with binary rewards, this comparison gives spatially different misses identical credit and yields no relative learning signal when every response fails. To address these limitations, we propose Spatial Credit Assignment (SCA), which uses click geometry to refine group-relative credit. Specifically, for a group with both successful and failed actions, SCA predicts each held-out click's reward from the other clicks and uses the prediction error to refine credit. If every sampled action fails, it instead ranks the clicks by distance to the annotated target; if every action succeeds, it keeps the standard group credit. The resulting credit weights the sampled responses in the policy update; spatial information is therefore used during training, while inference uses the policy alone. We then measure the error and directional alignment of the resulting policy updates relative to the exact return gradient in a controlled synthetic study. Across GUI grounding and offline action-prediction benchmarks, SCA improves performance across grounding domains and achieves the strongest results among reinforcement-fine-tuned models on most action-prediction metrics across the evaluated GUI suites, with consistent gains in our experiments.