Large vision-language models (LVLMs) perform well on long-horizon GUI tasks, but their high inference costs make small VLMs (SVLMs) attractive for cost-effective local deployment despite a substantial capability gap. Group Relative Policy Optimization (GRPO) is a widely used RL algorithm for improving SVLMs, yet its binary outcome rewards provide no intermediate progress signal across trajectories and its uniform advantages lead to semantically-blind credit assignment within each trajectory. We propose PAGE, an RL training method built on automatic progress reward construction and hierarchical advantage computation. The former derives structured evaluation criteria from successful trajectories through iterative extraction and produces stable continuous progress rewards through multi-round aggregation, while the latter combines progress-based inter-rollout advantages with intra-rollout subtask-level advantages obtained through LVLM-driven segmentation and completion verification. Across three long-horizon GUI scenarios, PAGE achieves a 79.3% average success rate with an 8B SVLM, a 16.0 pp gain over the GRPO baseline, and competitive performance against the evaluated closed-source LVLMs.