Update readme.md

Author: yhzheng1031

readme.md

@@ -11,7 +11,7 @@
 
 
 ## 🎯 Overview
-Autonomous GUI agents interact with environments by perceiving interfaces and executing actions.As a virtual sandbox, GUI World model empowers agents with human-like foresight by enabling action-conditioned prediction.However, existing text- and pixel- based approaches struggle to simultaneously achieve high visual fidelity and fine-grained structural controllability.To this end, we propose **Code2World**, a vision-language coder that simulates next visual state via **renderable code generation**. Especially, to address the data scarcity problem, we construct **AndroidCode** by translating GUI trajectories into high-fidelity HTML and refining synthesized code through a visual-feedback revision loop, resulting in **over 80K** high-quality screen-action pairs. To adapt existing VLMs into code prediction, we first perform SFT as a cold start for format layout following, then further apply **Render-Aware Reinforcement Learning** which uses the final rendered outcome by enforcing visual semantic fidelity and action consistency. Extensive experiments demonstrate that Code2World-8B achieves the top-performing next UI prediction, rivaling the competitive GPT-5 and Gemini-3-Pro-Image. Notably, \textit{Code2World significantly enhances downstream navigation success rates in a flexible manner}, boosting Gemini-2.5-Flash by {+9.5\%} on AndroidWorld navigation.
+Autonomous GUI agents interact with environments by perceiving interfaces and executing actions.As a virtual sandbox, GUI World model empowers agents with human-like foresight by enabling action-conditioned prediction.However, existing text- and pixel- based approaches struggle to simultaneously achieve high visual fidelity and fine-grained structural controllability.To this end, we propose **Code2World**, a vision-language coder that simulates next visual state via **renderable code generation**. Especially, to address the data scarcity problem, we construct **AndroidCode** by translating GUI trajectories into high-fidelity HTML and refining synthesized code through a visual-feedback revision loop, resulting in **over 80K** high-quality screen-action pairs. To adapt existing VLMs into code prediction, we first perform SFT as a cold start for format layout following, then further apply **Render-Aware Reinforcement Learning** which uses the final rendered outcome by enforcing visual semantic fidelity and action consistency. Extensive experiments demonstrate that Code2World-8B achieves the top-performing next UI prediction, rivaling the competitive GPT-5 and Gemini-3-Pro-Image. Notably, _Code2World significantly enhances downstream navigation success rates in a flexible manner_, boosting Gemini-2.5-Flash by **+9.5%** on AndroidWorld navigation.
 <!-- ![pipeline](figure/pipeline.jpg) -->
 ![teaser](figure/teaser.png)
 _Figure 1. Illustration of Code2World. Given a current GUI observation and an action, Code2World predicts the next screenshot via renderable code generation._