Improve model card: Add pipeline tag, library, abstract, and overview visuals

README.md

@@ -1,8 +1,34 @@
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 license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
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-## Introduction
-This repository contains the efficient GUI grounding model, **UI-S1-7B**, presented in [UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning](https://huggingface.co/papers/2509.11543).
+# UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning
 
-Project page: https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1
+This repository contains the efficient GUI grounding model, **UI-S1-7B**, presented in the paper [UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning](https://huggingface.co/papers/2509.11543).
+
+Project page / Code: [https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1](https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1)
+
+## Paper Abstract
+Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current approaches face a fundamental dilemma: offline RL enables stable training on pre-collected trajectories, but struggles with multi-step task execution for lack of trajectory-level reward signals; online RL captures these signals through environment interaction, but suffers from sparse rewards and prohibitive deployment costs. To address it, we present Semi-online Reinforcement Learning, a novel paradigm that simulates online RL on offline trajectories. During each rollout process, we preserve the original model output within the multi-turn dialogue, where a Patch Module adaptively recovers the divergence between rollout and expert trajectories. To capture long-term training signals, Semi-online RL introduces discounted future returns into the reward computation and optimizes the policy with weighted step-level and episode-level advantages. We further introduce Semi-Online Performance (SOP), a metric that aligns better with true online performance, serving as a practical and effective proxy for real-world evaluation. Experiments show that ours Semi-online RL achieves SOTA performance among 7B models across four dynamic benchmarks, with significant gains over the base model (e.g., +12.0% on AndroidWorld, +23.8% on AITW), demonstrating significant progress in bridging the gap between offline training efficiency and online multi-turn reasoning.
+
+## Overview
+
+We present **Semi-online RL**, a novel paradigm that simulates online reinforcement learning using offline trajectories, thereby enabling the efficient training of MLLM-based GUI agents with enhanced multi-turn interaction capabilities.
+
+<div align="center">
+  <img src="https://github.com/X-PLUG/MobileAgent/raw/main/UI-S1/assets/method_comparison.png" alt="Method Comparison" style="width:80%;">
+</div>
+
+Ours **UI-S1-7B** achieves SOTA performance on both semi-online metric (SOP) and online metric (AndroidWorld) among open-source 7B models.
+
+<div align="center">
+  <img src="https://github.com/X-PLUG/MobileAgent/raw/main/UI-S1/assets/metric.png" alt="Metrics" style="width:80%;">
+</div>
+
+## Detailed results
+
+<div align="center">
+  <img src="https://github.com/X-PLUG/MobileAgent/raw/main/UI-S1/assets/result.png" alt="Results" style="width:80%;">
+</div>