STEVE: AStep Verification Pipeline for Computer-use Agent Training
Developing AI agents to autonomously manipulate graphical user interfaces is
a long challenging task. Recent advances in data scaling law inspire us to
train computer-use agents with a scaled instruction set, yet using behavior
cloning to train agents still requires immense high-quality trajectories. To
meet the scalability need, we designed STEVE, a step verification pipeline for
computer-use agent training. First, we establish a large instruction set for
computer-use agents and collect trajectory data with some suboptimal agents.
GPT-4o is used to verify the correctness of each step in the trajectories based
on the screens before and after the action execution, assigning each step with
a binary label. Last, we adopt the Kahneman and Tversky Optimization to
optimize the agent from the binary stepwise labels. Extensive experiments
manifest that our agent outperforms supervised finetuning by leveraging both
positive and negative actions within a trajectory. Also, STEVE enables us to
train a 7B vision-language model as a computer-use agent, achieving leading
performance in the challenging live desktop environment WinAgentArena with
great efficiency at a reduced cost. Code and data:
https://github.com/FanbinLu/STEVE.
67da1df040371958e1732c83
Fanbin Lu
admin_assigned
2025-03-20T13:25:02.039Z
645b6094bc7518912e1fbc34
/avatars/5e1a875ba3ce350e71fe7049ca6a44c1.svg
Fanbin Lu
user
Fanbin
67da1df040371958e1732c84
Zhisheng Zhong
admin_assigned
2025-03-20T13:25:09.861Z
66371198c8950692a42f19d9
/avatars/64802642be361df8219f4f645f1335c4.svg
Zhisheng Zhong
user
ZhishengZhong
67da1df040371958e1732c85
Ziqin Wei
67da1df040371958e1732c86
Shu Liu
67da1df040371958e1732c87
Chi-Wing Fu
67da1df040371958e1732c88
Jiaya Jia
STEVE: AStep Verification Pipeline for Computer-use Agent Training
Developing AI agents to autonomously manipulate graphical user interfaces is
a long challenging task. Recent advances in data scaling law inspire us to
train computer-use agents with a scaled instruction set, yet using behavior
cloning to train agents still requires immense high-quality trajectories. To
meet the scalability need, we designed STEVE, a step verification pipeline for
computer-use agent training. First, we establish a large instruction set for
computer-use agents and collect trajectory data with some suboptimal agents.
GPT-4o is used to verify the correctness of each step in the trajectories based
on the screens before and after the action execution, assigning each step with
a binary label. Last, we adopt the Kahneman and Tversky Optimization to
optimize the agent from the binary stepwise labels. Extensive experiments
manifest that our agent outperforms supervised finetuning by leveraging both
positive and negative actions within a trajectory. Also, STEVE enables us to
train a 7B vision-language model as a computer-use agent, achieving leading
performance in the challenging live desktop environment WinAgentArena with
great efficiency at a reduced cost. Code and data:
https://github.com/FanbinLu/STEVE.
The STEVE pipeline utilizes step verification and Kahneman and Tversky Optimization to enhance computer-use agents trained from suboptimal trajectories, leading to superior performance in WinAgentArena.
STEVE step verification GPT-4o Kahneman and Tversky Optimization computer-use agents trajectory data vision-language model WinAgentArena