Graphical User Interface (GUI) agents powered by vision-language models (VLMs) have emerged as a promising paradigm for computer-use automation, but remain unreliable on long-horizon tasks. Existing planner–executor frameworks address this by shifting more reasoning to the planner, thereby decomposing rather than decoupling long-horizon reasoning. Our failure analysis reveals that many long-horizon failures are not pure capability gaps: from the same failure state, executors recover substantially more often when their accumulated context is reset with a locally executable subtask. To bridge this gap, we propose SCoR, a sparse context reset framework that restores the executor’s short-horizon capability by resetting its accumulated task context. At subtask boundaries, a lightweight Refiner emits a locally executable subtask and resets the Executor’s context; the executor then autonomously executes multiple GUI actions within the reset context. We train the Refiner with turn-level GRPO. In OSWorld, SCoR achieves a success rate of 53.16%, yielding a 25.41% relative improvement over the standalone EvoCUA-8B executor. Our code is available at https://github.com/huia711/SCoR.