UC Berkeley Researchers Release CUA-Lite, an Open Platform Unifying Sandboxes, Data, Evaluation and RL for Computer-Use Agents
UC Berkeley's CUA-Lite is an open platform unifying computer-use agent sandboxes, datasets, evaluation and RL; Lite.OSWorld cuts OSWorld memory 4.1 GB to 0.9 GB.
UC Berkeley researchers released CUA-Lite, an open platform placing agents, environments, traces, and training for computer-use agents behind one action space, one LiteSample schema, and one command across desktop, browser, and mobile. Lite.OSWorld reproduces the OSWorld task suite and evaluators in plain Docker containers (0.9 GB RAM vs 4.1 GB, cold start 23.8s, ~4.6× more parallel instances), with scores matching the QEMU/KVM VM across 13 models. The platform claims 30k+ verifiable tasks, 15+ benchmarks, 10+ agents, and 20+ datasets on Hugging Face including Aguvis, OpenCUA, and ScaleCUA. A documented SFT run lifts Qwen3-VL-2B-Instruct mean episode return from 0.138 to 0.237 on the 332-task lite.osworld split.
- Lite.OSWorld runs OSWorld tasks VM-free in Docker on any host without /dev/kvm
- Container scores match the OSWorld VM across 13 models, so training signal transfers
- 30k+ verifiable tasks, 15+ benchmarks, and 10+ agents integrated
- 10+ CUA datasets preprocessed into the LiteSample schema and published on Hugging Face
- Repository ships no explicit license yet; verify terms before commercial use
- Vendors
- UC Berkeley
- Organizations
- UC Berkeley
- AI models
- Qwen3-VL-2B-InstructUI-TARSFara-7B
From the original article
3 min read at marktechpost.com
Training and benchmarking a computer-use agent needs four things — agents, environments, traces, and a framework to evaluate and train them — and all four ship in incompatible formats today. CUA-Lite, from a UC Berkeley led team, puts them behind one action space and one data schema, and replaces OSWorld's per-task virtual machine with a plain Docker container at 0. 9 GB instead of 4.
By Asif Razzaq · marktechpost.com
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