title: STEVE: AStep Verification Pipeline for Computer-use Agent Training
summary: 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.
paper_authors._id: 67da1df040371958e1732c83
paper_authors.name: Fanbin Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T13:25:02.039Z
paper_authors._id: 645b6094bc7518912e1fbc34
paper_authors.avatarUrl: /avatars/5e1a875ba3ce350e71fe7049ca6a44c1.svg
paper_authors.fullname: Fanbin Lu
paper_authors.type: user
paper_authors.user: Fanbin
paper_authors._id: 67da1df040371958e1732c84
paper_authors.name: Zhisheng Zhong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T13:25:09.861Z
paper_authors._id: 66371198c8950692a42f19d9
paper_authors.avatarUrl: /avatars/64802642be361df8219f4f645f1335c4.svg
paper_authors.fullname: Zhisheng Zhong
paper_authors.type: user
paper_authors.user: ZhishengZhong
paper_authors._id: 67da1df040371958e1732c85
paper_authors.name: Ziqin Wei
paper_authors._id: 67da1df040371958e1732c86
paper_authors.name: Shu Liu
paper_authors._id: 67da1df040371958e1732c87
paper_authors.name: Chi-Wing Fu
paper_authors._id: 67da1df040371958e1732c88
paper_authors.name: Jiaya Jia
paper_title: STEVE: AStep Verification Pipeline for Computer-use Agent Training
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: STEVE step verification GPT-4o Kahneman and Tversky Optimization computer-use agents trajectory data vision-language model WinAgentArena