Cua just released Cua-S1-4B-0.2 today. Here's what you need to know. The Cua team, makers of the computer-use driver behind Hermes Agent, has open-sourced this multimodal decision model under Apache-2.0. It's described as the first multimodal decision model trained with RLOO (Reward-Weighted Leave-One-Out) on live computer-use tasks, using task-completion rewards. The model runs on a frozen Qwen3.5-4B base with LoRA adapters. At each step it takes in the screen state, the task goal, and a fixed set of candidate actions, then returns one action, no accessibility tree required. Training happened in two stages: supervised fine-tuning to teach the decision format, then agentic RL in live cua-bench-basic environments where only a fully completed task earns reward. On the GUI-360 benchmark, it scored 92.9% versus 60.1% for comparison. Text and multimodal adapters, training code, and benchmark results are all merged into the Cua GitHub repo and live on Hugging Face. This is version 0.2, up from 0.1 released the day before. Key numbers: - 4B parameters (Qwen3.5-4B base) - 92.9% vs 60.1% on GUI-360 benchmark - Apache-2.0 license - v0.1 to v0.2 in one day The model is small enough to run on-prem, and developers are already testing it on local hardware for business workflows.