Multimodal Reward Modeling

Long-horizon GUI automation remains challenging due to error accumulation over extended interaction sequences. Process Reward Models (PRMs) provide dense step-level supervision for mitigating error accumulation, yet standard PRMs are poorly suited to GUI verification. Standard PRM judgments often rely on superficial visual alignment rather than functional correctness, reflecting an evaluative knowledge gap caused by missing domain-specific adjudication logic. Standard PRMs also perform passive, single-pass visual assessment, which creates Visual Ambiguity when reliable judgment requires actively locating, parsing, or inspecting task-relevant UI evidence. We introduce GUI-PRA, a Process Reward Agent that transforms GUI process evaluation from passive scoring into active investigation. GUI-PRA couples Experience-Injected Criterion Synthesis, which distills generalized verification principles into state-specific criteria, with Criterion-Guided Autoregressive Perception, which uses these criteria to navigate multi-granularity visual tools and gather grounded evidence. On AndroidWorld and Mobile-MiniWoB++, GUI-PRA achieves improvements of 5.0% and 6.5% over standard PRMs on the Qwen-VL series, with Qwen3-VL attaining 54.74% success rate on AndroidWorld. On the offline OS-Critic Bench, GUI-PRA demonstrates strong competitiveness against fully trained critic models.

Process Reward Models (PRMs) provide fine-grained supervision over intermediate reasoning steps, yet most existing models lack long-range reasoning and deep thinking capabilities. Although prior work has introduced Chain-of-Thought (CoT) to reward models, the high annotation cost of CoT-PRM still limits its scalability. To address these challenges, we propose the Visual Reasoning Process Reward Model (VRPRM) and design a label-efficient two-stage training strategy. Experimental results demonstrate that with only 5.5K CoT-PRM supervised step labels and 40K non-CoT PRM reinforcement learning training examples, VRPRM achieves a macro F1 score of 69.18 on VisualProcessBench, outperforming VisualPRM-8B, a non-thinking PRM trained on 400K examples, by 7.00 points. In Best-of-N evaluation, VRPRM-RL brings absolute Overall improvements of 21.77, 23.42, and 23.12 points on InternVL2.5-8B, 26B, and 38B policies, respectively, with a maximum relative improvement of 59.0%.