UC Berkeley Researchers Release CUA-Lite, an Open Platform Unifying Sandboxes, Data, Evaluation and RL for Computer-Use Agents
UC Berkeley just unified the fragmented tooling for building computer-use agents — one action space, one data schema, Docker instead of 4GB VMs.
Why it matters
CUA-Lite removes infrastructure friction that has split the agent-training ecosystem into incompatible silos. Practitioners building or fine-tuning computer-use agents now have a standard platform for sandbox environments, training data, evaluation, and reinforcement learning — lowering the barrier to agent development and enabling broader experimentation.
The key facts
6 to knowCUA-Lite unifies agents, environments, traces, and evaluation framework behind one action space and data schema
Replaces per-task virtual machines with Docker containers: 0.9 GB vs 4.1 GB (78% reduction)
Addresses fragmentation: today's sandboxes, data formats, and RL frameworks are incompatible
UC Berkeley led team
Open-source platform
Replaces OSWorld's infrastructure approach
Go to the source
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Publisher excerpt: 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…