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inclusionAI
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Figure: The Four-Stage Training Pipeline of UI-Venus-1.5. Starting from the Qwen3-VL Series, the model undergoes a progressive training curriculum comprising: (1) Mid-Training with large-scale GUI data for domain knowledge injection; (2) Offline-RL with task-specific optimization across grounding, mobile, and web objectives; (3) Online-RL to further enhance navigation capabilities in complex, real-world scenarios; and (4) Model Merge to unify the specialized models into the final UI-Venus-1.5.
Compared to our previous version(UI-Venus 1.0), UI-Venus-1.5 introduces three key technical advances:
Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6), VenusBench-GD (75.0), and AndroidWorld (77.6), significantly outperforming previous strong baselines. In addition, UI-Venus-1.5 demonstrates robust navigation capabilities across 40+ Chinese mobile apps, effectively executing user instructions in real-world scenarios.
You can deploy the model using vLLM (requires vllm>=0.11.0 and transformers>=4.57.0):
bash
# Install vLLMpip install vllm # Start vLLM API serverpython -m vllm.entrypoints.openai.api_server \ --model inclusionAI/UI-Venus-1.5-30B-A3B \ --served-model-name UI-Venus-1.5-30B-A3B \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 2 \ --trust-remote-codeThe model will be served at http://localhost:8000/v1.
Please consider citing if you find our work useful:
plain
@misc{venusteam2026uivenus15technicalreport, title={UI-Venus-1.5 Technical Report}, author={Venus Team and Changlong Gao and Zhangxuan Gu and Yulin Liu and Xinyu Qiu and Shuheng Shen and Yue Wen and Tianyu Xia and Zhenyu Xu and Zhengwen Zeng and Beitong Zhou and Xingran Zhou and Weizhi Chen and Sunhao Dai and Jingya Dou and Yichen Gong and Yuan Guo and Zhenlin Guo and Feng Li and Qian Li and Jinzhen Lin and Yuqi Zhou and Linchao Zhu and Liang Chen and Zhenyu Guo and Changhua Meng and Weiqiang Wang}, year={2026}, eprint={2602.09082}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2602.09082}, } @misc{gu2025uivenustechnicalreportbuilding, title={UI-Venus Technical Report: Building High-performance UI Agents with RFT}, author={Zhangxuan Gu and Zhengwen Zeng and Zhenyu Xu and Xingran Zhou and Shuheng Shen and Yunfei Liu and Beitong Zhou and Changhua Meng and Tianyu Xia and Weizhi Chen and Yue Wen and Jingya Dou and Fei Tang and Jinzhen Lin and Yulin Liu and Zhenlin Guo and Yichen Gong and Heng Jia and Changlong Gao and Yuan Guo and Yong Deng and Zhenyu Guo and Liang Chen and Weiqiang Wang}, year={2025}, eprint={2508.10833}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2508.10833}, }# Install vLLMpip install vllm # Start vLLM API serverpython -m vllm.entrypoints.openai.api_server \ --model inclusionAI/UI-Venus-1.5-30B-A3B \ --served-model-name UI-Venus-1.5-30B-A3B \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 2 \ --trust-remote-code@misc{venusteam2026uivenus15technicalreport, title={UI-Venus-1.5 Technical Report}, author={Venus Team and Changlong Gao and Zhangxuan Gu and Yulin Liu and Xinyu Qiu and Shuheng Shen and Yue Wen and Tianyu Xia and Zhenyu Xu and Zhengwen Zeng and Beitong Zhou and Xingran Zhou and Weizhi Chen and Sunhao Dai and Jingya Dou and Yichen Gong and Yuan Guo and Zhenlin Guo and Feng Li and Qian Li and Jinzhen Lin and Yuqi Zhou and Linchao Zhu and Liang Chen and Zhenyu Guo and Changhua Meng and Weiqiang Wang}, year={2026}, eprint={2602.09082}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2602.09082}, }@misc{gu2025uivenustechnicalreportbuilding, title={UI-Venus Technical Report: Building High-performance UI Agents with RFT}, author={Zhangxuan Gu and Zhengwen Zeng and Zhenyu Xu and Xingran Zhou and Shuheng Shen and Yunfei Liu and Beitong Zhou and Changhua Meng and Tianyu Xia and Weizhi Chen and Yue Wen and Jingya Dou and Fei Tang and Jinzhen Lin and Yulin Liu and Zhenlin Guo and Yichen Gong and Heng Jia and Changlong Gao and Yuan Guo and Yong Deng and Zhenyu Guo and Liang Chen and Weiqiang Wang}, year={2025}, eprint={2508.10833}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2508.10833}, }