UC Berkeley Researchers Release CUA-Lite, an Open Platform Unifying Sandboxes, Data, Evaluation and RL for Computer-Use Agents
A team of researchers from UC Berkeley have released CUA-Lite, an open platform for computer-use agents (CUAs). The argument behind it is infrastructural rather than model-centric: training and benchmarking a CUA requires four pieces: agents, environments, traces, and a framework to evaluate and train them and all four are currently fragmented across separate repositories with incompatible interfaces. CUA-Lite puts them behind one action space, one data schema, and one command, across desktop,
UC Berkeley researchers released CUA-Lite, an open platform designed to unify the fragmented tools needed to train and test computer-use agents, which are AI systems that interact with digital interfaces. The platform consolidates agents, environments, task data, and evaluation frameworks behind a single action space and data schema that work across desktop, browser, and mobile platforms. A key technical contribution is Lite.OSWorld, which runs desktop tasks in lightweight Docker containers instead of full virtual machines, reducing memory requirements from 4.1 GB to 0.9 GB while maintaining evaluation score equivalence with the original benchmark. The platform includes over 30,000 verifiable tasks, integrates 15 or more existing benchmarks, and publishes datasets freely on Hugging Face, making it deployable on standard cloud infrastructure without specialized virtualization requirements.
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