STEVE: AStep Verification Pipeline for Computer-use Agent Training
Authors: name: Fanbin Lu, name: Zhisheng Zhong, name: Ziqin Wei, name: Shu Liu, name: Chi-Wing Fu, name: Jiaya Jia

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.