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Scaling Agentic Data for Long-Horizon Terminal Intelligence

132 upvotes · 9 OCT 2026 · Shun Zou, Ziyu Ma, Yi Zou et al.

This paper introduces a method to generate high-quality long-horizon trajectories for terminal agents, allowing them to complete complex tasks in a realistic and autonomous way. Practitioners in AI/ML research and development can benefit from this approach to improve the performance and reliability of autonomous systems.

agentic datalong-horizon trajectoriescontinual learningroboticsagentsbenchmarks