ANCHOR: Branch-Point Data Generation for GUI Agents
End-to-end GUI agents for real desktop environments require large amounts of high-quality interaction data, yet collecting human demonstrations is expensive and existing synthetic pipelines often suffer from limited task diversity or noisy, goal-drifting trajectories. We present a trajectory expansion framework Anchor that bootstraps scalable desktop supervision from a small set of verified seed demonstrations. Starting from each seed, we identify branch points that correspond to meaningful state changes and propose new, state-grounded task variants conditioned on the current GUI context. An executing agent then follows the proposed instructions to generate new trajectories, while a verifier enforces task completion via state-aware checks and trajectory-level consistency. To improve supervision quality, we further apply task-conditioned step-level filtering to remove ungrounded actions and denoise post-branch segments to maintain coherent intent. Experiments on standard desktop benchmarks, OSWorld and WindowsAgentArena, show that models fine-tuned on our expanded corpus achieve consistent improvements over zero-shot agents and representative synthesis baselines, and generalize across applications and operating systems.
698abd921b2dc6b37d61b120
Jinbiao Wei
admin_assigned
2026-02-10T19:44:23.938Z
683c642b02c1a474a867964e
/avatars/63e44a9cf788ee7b3ad236407700ceca.svg
Jinbiao Wei
mikeweii
user
mikeweii
698abd921b2dc6b37d61b121
Yilun Zhao
698abd921b2dc6b37d61b122
Kangqi Ni
698abd921b2dc6b37d61b123
Arman Cohan
ANCHOR: Branch-Point Data Generation for GUI Agents
End-to-end GUI agents for real desktop environments require large amounts of high-quality interaction data, yet collecting human demonstrations is expensive and existing synthetic pipelines often suffer from limited task diversity or noisy, goal-drifting trajectories. We present a trajectory expansion framework Anchor that bootstraps scalable desktop supervision from a small set of verified seed demonstrations. Starting from each seed, we identify branch points that correspond to meaningful state changes and propose new, state-grounded task variants conditioned on the current GUI context. An executing agent then follows the proposed instructions to generate new trajectories, while a verifier enforces task completion via state-aware checks and trajectory-level consistency. To improve supervision quality, we further apply task-conditioned step-level filtering to remove ungrounded actions and denoise post-branch segments to maintain coherent intent. Experiments on standard desktop benchmarks, OSWorld and WindowsAgentArena, show that models fine-tuned on our expanded corpus achieve consistent improvements over zero-shot agents and representative synthesis baselines, and generalize across applications and operating systems.
A trajectory expansion framework called Anchor bootstraps scalable desktop supervision from seed demonstrations by identifying branch points and generating new trajectories through state-grounded task variants.
trajectory expansion GUI agents desktop environments seed demonstrations branch points state-grounded task variants trajectory generation verifier task completion step-level filtering denoising