arXiv:2605.29119v1 Announce Type: new Abstract: Computer use agents (CUAs) have shown strong potential for automating complex digital workflows, yet their training remains constrained by costly live environment interaction and limited high-quality supervision. Existing filtered behavior cloning pipelines suffer from imitation bottlenecks, including distribution shift from the expert demonstration and the absence of negative learning signals. Meanwhile, standard trajectory-level reinforcement learning struggles with sparse rewards, ambiguous credit assignment, and high infrastructure costs for
The continuous evolution of AI agents necessitates better training paradigms to move beyond current limitations of costly interaction and supervision.
Improved training methods for computer use agents can unlock more sophisticated automation of complex digital workflows, fundamentally changing how businesses operate.
The development of PRO-CUA represents a step towards more efficient and robust agent training, potentially accelerating the deployment of highly autonomous AI agents.
- · AI software developers
- · Businesses adopting automation
- · Cloud computing providers
- · Human task handlers in digital workflows
- · Legacy automation software vendors
More capable and reliable AI agents become widely deployable.
Significant productivity gains across various industries due to advanced workflow automation.
The definition of 'work' continues to shift as AI agents take on increasingly complex cognitive tasks, leading to societal re-evaluation of labor.
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Read at arXiv cs.AI