CUA-Master framework trains computer-use agents with recursive data loops

A team of researchers has published CUA-Master, a training framework for computer-use agents that pairs an outer loop, which keeps pulling in open-world material and targets whatever the model currently handles poorly, with an inner loop that turns real workflows mined from instructional videos into reusable building blocks for generating new tasks. Trajectories are gathered in live environments using a retry scheme that rolls back to earlier snapshots when a run fails. The work is a preprint on arXiv; no code or checkpoint release is described in the abstract.