Improve model card: Add abstract and prominent GitHub link (#1)

- Improve model card: Add abstract and prominent GitHub link (f7831e65a381a737f0322cc6cd266043b62b5e42)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -3,20 +3,28 @@ base_model:
 - Qwen/Qwen2.5-VL-3B-Instruct
 language:
 - en
+library_name: transformers
 license: apache-2.0
+pipeline_tag: image-text-to-text
 tags:
 - gui
 - agent
 - gui-grounding
 - reinforcement-learning
-pipeline_tag: image-text-to-text
-library_name: transformers
 ---
 
 # InfiGUI-G1-3B
 
 This repository contains the InfiGUI-G1-3B model from the paper **[InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization](https://arxiv.org/abs/2508.05731)**.
 
+[![GitHub Repo](https://img.shields.io/badge/GitHub-Repo-181717?style=flat&logo=github&logoColor=white)](https://github.com/InfiXAI/InfiGUI-G1)
+
+## Paper Abstract
+
+The emergence of Multimodal Large Language Models (MLLMs) has propelled the development of autonomous agents that operate on Graphical User Interfaces (GUIs) using pure visual input. A fundamental challenge is robustly grounding natural language instructions. This requires a precise spatial alignment, which accurately locates the coordinates of each element, and, more critically, a correct semantic alignment, which matches the instructions to the functionally appropriate UI element. Although Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be effective at improving spatial alignment for these MLLMs, we find that inefficient exploration bottlenecks semantic alignment, which prevent models from learning difficult semantic associations. To address this exploration problem, we present Adaptive Exploration Policy Optimization (AEPO), a new policy optimization framework. AEPO employs a multi-answer generation strategy to enforce broader exploration, which is then guided by a theoretically grounded Adaptive Exploration Reward (AER) function derived from first principles of efficiency eta=U/C. Our AEPO-trained models, InfiGUI-G1-3B and InfiGUI-G1-7B, establish new state-of-the-art results across multiple challenging GUI grounding benchmarks, achieving significant relative improvements of up to 9.0% against the naive RLVR baseline on benchmarks designed to test generalization and semantic understanding. Resources are available at this https URL .
+
+## Model Description
+
 The model is based on `Qwen2.5-VL-3B-Instruct` and is fine-tuned using our proposed **Adaptive Exploration Policy Optimization (AEPO)** framework. AEPO is a novel reinforcement learning method designed to enhance the model's **semantic alignment** for GUI grounding tasks. It overcomes the exploration bottlenecks of standard RLVR methods by integrating a multi-answer generation strategy with a theoretically-grounded adaptive reward function, enabling more effective and efficient learning for complex GUI interactions.
 
 ## Quick Start
@@ -72,7 +80,7 @@ def load_image(img_path: str) -> Image.Image:
 
 
 def visualize_points(original_image: Image.Image, points: list, 
-                    new_width: int, new_height: int,
+                    new_width: int, new_height: int,\
                     original_width: int, original_height: int) -> None:
     """Draw prediction points on original image and save as output.png."""
     output_img = original_image.copy()
@@ -90,8 +98,8 @@ def visualize_points(original_image: Image.Image, points: list,
         
         # Draw circle
         circle_radius = 20
-        draw.ellipse([original_x - circle_radius, original_y - circle_radius,
-                     original_x + circle_radius, original_y + circle_radius],
+        draw.ellipse([original_x - circle_radius, original_y - circle_radius,\
+                     original_x + circle_radius, original_y + circle_radius],\
                     fill=(255, 0, 0))
         
         # Draw label
@@ -125,7 +133,8 @@ def main():
 
     # Prepare model inputs
     instruction = "shuffle play the current playlist"
-    system_prompt = 'You FIRST think about the reasoning process as an internal monologue and then provide the final answer.\nThe reasoning process MUST BE enclosed within <think> </think> tags.'
+    system_prompt = 'You FIRST think about the reasoning process as an internal monologue and then provide the final answer.\
+The reasoning process MUST BE enclosed within <think> </think> tags.'
     prompt = f'''The screen's resolution is {new_width}x{new_height}.
 Locate the UI element(s) for "{instruction}", output the coordinates using JSON format: [{{"point_2d": [x, y]}}, ...]'''
 
@@ -164,8 +173,6 @@ if __name__ == "__main__":
 
 To reproduce the results in our paper, please refer to our repo for detailed instructions.
 
-For more details on the methodology and evaluation, please refer to our [paper](https://arxiv.org/abs/2508.05731) and [repository](https://github.com/InfiXAI/InfiGUI-G1).
-
 ## Results
 
 Our InfiGUI-G1 models, trained with the AEPO framework, establish new state-of-the-art results among open-source models across a diverse and challenging set of GUI grounding benchmarks.
@@ -242,4 +249,4 @@ If you find this work useful, we would be grateful if you consider citing the fo
   journal={arXiv preprint arXiv:2501.04575},
   year={2025}
 }
-```
+```