UI-Venus Technical Report: Building High-performance UI Agents with RFT
Authors: name: Zhangxuan Gu; name: Zhengwen Zeng; name: Zhenyu Xu; name: Xingran Zhou; name: Shuheng Shen; name: Yunfei Liu; name: Beitong Zhou; name: Changhua Meng; name: Tianyu Xia; name: Weizhi Chen; name: Yue Wen; name: Jingya Dou; name: Fei Tang; name: Jinzhen Lin; name: Yulin Liu; name: Zhenlin Guo; name: Yichen Gong; name: Heng Jia; name: Changlong Gao; name: Yuan Guo; name: Yong Deng; name: Zhenyu Guo; name: Liang Chen; name: Weiqiang Wang
Published: 2025-08-14 16:58:07

Abstract: We present UI-Venus, a native UI agent that takes only screenshots as input
based on a multimodal large language model. UI-Venus achieves SOTA performance
on both UI grounding and navigation tasks using only several hundred thousand
high-quality training samples through reinforcement finetune (RFT) based on
Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% /
50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e.,
Screenspot-V2 / Pro, surpassing the previous SOTA baselines including
open-source GTA1 and closed-source UI-TARS-1.5.To show UI-Venus's summary and
planing ability, we also evaluate it on the AndroidWorld, an online UI
navigation arena, on which our 7B and 72B variants achieve 49.1% and 65.9%
success rate, also beating existing models.To achieve this, we introduce
carefully designed reward functions for both UI grounding and navigation tasks
and corresponding efficient data cleaning strategies.To further boost
navigation performance, we propose Self-Evolving Trajectory History Alignment
\& Sparse Action Enhancement that refine historical reasoning traces and
balances the distribution of sparse but critical actions, leading to more
coherent planning and better generalization in complex UI tasks. Our
contributions include the publish of SOTA open-source UI agents, comprehensive
data cleaning protocols and a novel self-evolving framework for improving
navigation performance, which encourage further research and development in the
community. Code is available at https://github.com/antgroup/UI-Venus.