CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer-Use Agent with Decoupled Reinforcement Learning

Autonomous agents for Graphical User Interfaces (GUIs) face significant challenges in novel software, requiring both long-horizon planning with software domain knowledge and precise, fine-grained execution. Existing approaches suffer from a trade-off: generalist agents excel at planning but falter in execution, while specialized agents show the opposite weakness. Recent compositional frameworks attempt to bridge this gap by combining a “planner” and an “actor”, but they are typically static and non-trainable, preventing adaptation from experience—a critical limitation given the scarcity of high-quality data in novel software. To address these limitations, we introduce CODA, a novel and trainable compositional framework that synergizes a generalist planner (Cerebrum) with a specialist executor (Cerebellum), trained with a dedicated two-stage training pipeline. The first stage, Specialization, employs a decoupled GRPO approach to train an expert planner for each novel software individually. The second stage, Generalization, aggregates all positive trajectories from all specialized experts. This consolidated, high-quality dataset is then used to perform supervised fine-tuning (SFT) on the final planner, equipping it with the robust, cross-domain capabilities of a generalist. Evaluated on the ScienceBoard benchmark with diversified novel software, our framework significantly outperforms the baseline and establishes a new state-of-the-art (SOTA) among open-source models, with strong generalizability to novel software and unseen executors such as code agents. All code and models will be made publicly available to foster further research.