ActIO-UI-7B-RLVR is a model trained for web GUI subtask execution.
We introduce it in the paper WARC-Bench: Web Archive based Benchmark for GUI Subtask Executions.
Its base model is Qwen2.5-VL-7B-Instruct.
We define a GUI subtask as a short-horizon web interaction task that correspond to simple natural language instructions within a larger web browsing workflow.
Some examples of subtasks include choosing the correct date in a date picker or scrolling in a container to extract information.
ActIO-UI-7B-RLVR achieves state-of-the-art (SOTA) results for open-source models on WARC-Bench, our GUI subtask benchmark.
It also exhibits improved long-horizon GUI task and grounding capabilities when compared with its base model. We provide a performance summary of the ActIO-UI model family below.
Model
WARC-Bench (test split)
WebArena (no map)
Miniwob++
ScreenSpot V2
ActIO-UI-7B-SFT
27.33 %
13.40 %
29.60 %
80.03 %
ActIO-UI-7B-RLVR
29.17 %
7.31 %
36.27 %
75.81 %
ActIO-UI-72B-SFT
48.33 %
23.05 %
45.87 %
84.12 %
ActIO-UI-72B-RLVR
52.33 %
26.80 %
59.20 %
82.44 %
ActIO-UI is developed by Orby AI, a Uniphore company. Please direct all correspondence to Sanjari Srivastava and Peng Qi (first_name.last_name at uniphore.com).
Model Family
ActIO-UI-7B-SFT: a 7B model trained with supervised finetuning (SFT) using distilled subtask data.
ActIO-UI-7B-RLVR: a 7B model trained with Reinforcement Learning with Verifiable Rewards (RLVR) over the ActIO-UI-7B-SFT checkpoint.
ActIO-UI-72B-SFT (private due to Qwen licensing restrictions): a 72B model trained with SFT using distilled subtask data.
ActIO-UI-72B-RLVR (private due to Qwen licensing restrictions): a 72B model trained with RLVR over the ActIO-UI-72B-SFT checkpoint.
Performance
GUI Subtask (WARC-Bench)
ActIO-UI models are specifically trained to solve GUI subtask problems. Both the 7B and 72B ActIO-UI RLVR models achieves SOTA performance for their respective sub-categories of 7B or 72B open-source models.
Model
Dev [synthetic]
Dev [real]
Dev [total]
Test
Closed-source
OpenAI computer-use-preview (2025-03-11)CUA
62.17
49.44
58.96
33.83
GPT-4o (2024-11-20)
7.87
14.51
9.54
9.17
GPT-5 (2025-08-07)
72.66
61.67
69.89
51.33
Claude Sonnet 4.0 (2025-05-14)CUA
79.92
76.11
78.96
47.17
Claude Sonnet 3.7 (2025-02-19)
82.96
78.89
81.93
59.83
Claude Sonnet 4.0 (2025-05-14)
84.27
81.67
83.61
64.83
Open-source — Small
Qwen2.5-VL 7B
16.85
11.67
15.54
4.67
UI-Tars 1.5 7BCUA
44.01
26.55
39.66
10.33
OpenCUA 7BCUA*
48.03
41.67
46.43
14.00
ActIO-UI-7B-SFT
70.60
54.49
66.54
27.33
ActIO-UI-7B-RLVR
78.09
54.44
72.13
29.17
Open-source — Large
OpenCUA 32BCUA*
51.12
41.67
48.74
17.50
Qwen2.5-VL 72B
64.23
51.67
61.06
37.33
ActIO-UI-72B-SFT
78.23
68.89
75.88
48.33
ActIO-UI-72B-RLVR
87.64
78.33
84.31
52.33
Trajectory-level success rates on WARC-Bench. Small VLMs (7B params) are grouped under “Open-source — Small”. Results are divided into closed (top) vs. open-source (bottom). CUA = evaluated with provider’s computer-use agent; others use our Subtask Vision Agent (SVA) design. Rows marked with * denote averages across fewer than 3 runs; all others are averages of 3 runs. Bold = best per benchmark. Underline = best inside its sector (closed; open-source small; open-source large).
Other Benchmarks
To access generalizability of GUI subtask execution as a model capability, we compare the performance of ActIO-UI over GUI subtasks (WARC-Bench), long-horizon tasks (WebArena), short-horizon tasks (Miniwob++), and GUI visual grounding (ScreenSpot V2). Without access to any long-horizon and grounding data in its training dataset, our models show improved performance over their base models (except for the grounding performance when compared to Qwen 2.5 VL 72B).
Model
WARC-Bench (test split)
WebArena (no map)
Miniwob++
ScreenSpot V2
Public Models
Qwen 2.5 VL 7B
4.67 %
3.07 %
12.53 %
51.62 %
Qwen 2.5 VL 72B
37.33 %
15.68 %
53.87 %
88.05 %
GPT-5 (2025-08-07)
51.33 %
34.06 %
52.27 %
26.39 %
Claude 4 Sonnet
64.83 %
37.96 %
71.73 %
85.06 %
Ours
ActIO-UI-7B-SFT
27.33 %
13.40 %
29.60 %
80.03 %
ActIO-UI-7B-RLVR
29.17 %
7.31 %
36.27 %
75.81 %
ActIO-UI-72B-SFT
48.33 %
23.05 %
45.87 %
84.12 %
ActIO-UI-72B-RLVR
52.33 %
26.80 %
59.20 %
82.44 %
Each number is an average of 3 runs. Bold = best per benchmark. We omit the OpenStreetMap portion of the WebArena benchmark due to technical difficulties with the online map service. For Screenspot V2, we limit the agent action space to \texttt{click(...)} only. For each evaluation, we use the same prompt format built inside our Subtask Vision Agent
Usage
Image Input Size
To maintain optimal model performance, each input image should be set at 1280 (pixel width) \(\times\) 720 (pixel height).
Setup
To run the code snippets below, we recommend that you install everything in requirements.txt in a python environment.
You can make an inference call to the model with the sanity.py file included in this repository.
python sanity.py
Note that this is only a sanity test for ensuring model is working properly.
For replicating the evaluation result or using the model for your own project, please refer to our code repository on GitHub.
Legal
License
This project is licensed under the Open Model, Data, & Weights License Agreement (OpenMDW). See the LICENSE file in the root folder for details.
The authors, contributors, and copyright holders are not responsible for any illegal, unethical, or harmful use of the Software, nor for any direct or indirect damages resulting from such use.
Use of the name, logo, or trademarks of "ActIO", "ActIO-UI" "WARC-Bench", or "Uniphore" does not imply any endorsement or affiliation unless separate written permission is obtained.
Users are solely responsible for ensuring their use complies with applicable laws and regulations.
Value Statement
The model should not be used for any purpose or activity that violates applicable laws or regulations in any jurisdiction.
Use for illegal, unethical, or harmful activities should be prohibited.
ActIO-UI are intended for research and educational purposes only.
Citation
If you find our work useful, please cite with
@misc{srivastava2025warcbenchwebarchivebased,
title={WARC-Bench: Web Archive Based Benchmark for GUI Subtask Executions},
author={Sanjari Srivastava and Gang Li and Cheng Chang and Rishu Garg and Manpreet Kaur and Charlene Y. Lee and Yuezhang Li and Yining Mao and Ignacio Cases and Yanan Xie and Peng Qi},
year={2025},
eprint={2510.09872},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2510.09872},
}
