Berkeley researchers open-source unified platform for computer-use agents

A UC Berkeley-led team released CUA-Lite, an open platform that standardizes the sandboxes, data formats, evaluation, and reinforcement-learning setups used to train and benchmark computer-use agents. It replaces OSWorld's per-task virtual machines with lightweight Docker containers, cutting the per-task footprint from 4.1GB to 0.9GB.

Why it matters: Computer-use agent research has been fragmented across incompatible environments and data schemas, making results hard to compare across labs. A shared, lightweight standard could accelerate progress the way common benchmarks did for earlier ML subfields, and the smaller footprint makes large-scale RL training meaningfully cheaper to run.