UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026 · 1 citation)
Multimodal GUI agents perform well on short mobile tasks but break down on long, memory-heavy ones. Existing 7B models lose about 90.9% of their accuracy on MemGUI-Bench. The authors trace these failures to three causes: forgetting earlier observations, losing track of task progress, and making arithmetic mistakes.
Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026)
Training vision-based GUI agents for long, multi-step tasks involves a trade-off. Single-turn RL methods such as GRPO learn from expert histories, but at deployment the agent must rely on its own past actions, so training and real use differ. Online multi-turn RL closes this gap, but it brings sparse rewards, high…

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Updated 1 November 2026