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, browser and mobile.
Is it deployable? Yes. The stack installs with uv sync --all-extras on Python 3.12, and its lightweight sandboxes run on any Docker host without /dev/kvm, so cloud instances, CI runners and nested containers all work.
The VM tax, and how Lite.OSWorld removes it #
The most concrete contribution is Lite.OSWorld. OSWorld provides a faithful Ubuntu desktop, but it ships as a full QEMU/KVM virtual machine per task, requiring nested virtualization that most managed infrastructure does not expose. CUA-Lite reproduces the same task suite and the same evaluators on a GNOME desktop inside a plain Docker container.
| Task | OSWorld | Lite.OSWorld |
|---|---|---|
| Runtime | QEMU/KVM VM | Docker container |
| Host requirement | /dev/kvm , nested virt |
Any Docker host |
| Memory | 4.1 GB | 0.9 GB |
| Cold start | 29.9 s | 23.8 s |
| Parallelism | baseline | ~4.6× more instances |
| Task suite | OSWorld | Identical |
Fidelity is the obvious concern when you swap a VM for a container, and the team addresses it directly: across 13 models, Lite.OSWorld scores match the OSWorld VM’s, so a score or a training signal earned in the container transfers back to the real benchmark. The same base now carries a family of sandboxes: Lite.ScaleCUA, Lite.CUAGym and Lite.CUAWorld, the last expanding into roughly 40 applications including Blender, QGIS and VS Code. In total the platform claims 30k+ verifiable tasks.
One schema for data, one adapter per model #
CUA-Lite’s second layer is LiteSample, a single supervised-learning schema shared across every environment, agent and task type, shipped as plain parquet plus images. Ten-plus existing CUA datasets have been preprocessed into it and published free on Hugging Face, including Aguvis, OpenCUA, ScaleCUA, GUI-360, GUIOdyssey and Multimodal-Mind2Web. Alongside those corpora sit fresh rollout datasets generated by rolling a frontier teacher model through the sandboxes, for distillation into smaller students.
Because model families expect different scaffolding, the framework ships a per-model adapter that packs a unified LiteSample into each model’s own training format, including history collapsing so several steps share one forward pass.
Eval, SFT and RL behind one command #
Agents and environments meet in lite.gym: screenshots up, actions down, with one action space per platform. 10+ agents are built in GPT, Claude, Gemini, Qwen3-VL, UI-TARS, Fara-7B, MAI-UI and others, and 15+ benchmarks are integrated, spanning grounding (ScreenSpot-Pro, OSWorld-G), desktop (OSWorld, OSWorld-2, WindowsAgentArena, CUABench), browser (WebArena, VisualWebArena, MiniWoB, WebVoyager, Online-Mind2Web, WebGym) and mobile (AndroidWorld, AndroidLab, MobileWorld, MobileGym). Swapping --model-id and --env-id in scripts/rollout.py is the whole interface.
The same loop serves training. For SFT, the README documents fine-tuning Qwen3-VL-2B-Instruct on Lite.ScaleCUA desktop trajectories, lifting mean episode return from 0.138 to 0.237 on the 332-task lite.osworld eval split, a single reported configuration on two GPUs, not an independently reproduced result. For RL, rollouts scored in the environment drive GRPO updates on top of Slime, with a worked MobileGym example covering 416 mobile tasks across 28 apps.
Interactive explainer #
Key Takeaways #
- CUA-Lite unifies agents, environments, traces and training under one action space and one
LiteSampleschema. - Lite.OSWorld runs OSWorld tasks VM-free in Docker at 0.9 GB versus 4.1 GB, roughly 4.6× more parallel desktops.
- Scores in the container match the OSWorld VM across 13 models, so training signal transfers to the real benchmark.
- 30k+ verifiable tasks, 15+ benchmarks, 10+ agents, and 20+ datasets published free on Hugging Face.
- Deployable on any Docker host, but the repository ships no explicit license yet — verify terms before commercial use.
Check out the Project Page, ** GitHub Repo** and ** Datasets on Hugging Face**. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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