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EvoCUA-1.5 addresses these challenges with Step-Level Policy Optimization (STEPO), which preserves traj","title":"EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents","url":"https://arxiv.org/abs/2607.09773","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.09773v1 Announce Type: new \nAbstract: Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although imitation learning and offline trajectory refinement provide strong priors, static traces cannot cover the causal feedback loop of real computer use: each action changes the screen state, future action space, and recovery options. EvoCUA-1.5 extends self-evolving computer-use agents from offline experience learning to online reinforcement learning, where policies interact with executable sandbox environments and improve from verifiable task outcomes. 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