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FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

115 upvotes · 19 AUG 2026 · Kou Shi, Zun Wang, Qisheng Su et al.

This paper develops a method to generate high-quality terminal tasks for training agents, ensuring that the tasks accurately reflect the original instruction and environment, and providing a way to validate and improve the generated tasks. Practitioners in AI and robotics may care about this work because it addresses a common challenge in training terminal agents.

continual learningscalable executable supervisionterminal task synthesismulti-stage synthesisagent trainingtask validation