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This paper proposes WRIT, a pipeline for synthesizing multi-turn agent training trajectories that balance write-intensive and read-heavy complexity. The method generates diverse tasks and simulations, enabling small models to achieve strong performance with reduced inference cost.
GTA-2 introduces a hierarchical benchmark for evaluating general tool agents across atomic tool-use and open-ended workflows, revealing a significant capability cliff where frontier models achieve only 14.39% success on complex tasks despite reasonable atomic performance.