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

Authors

Do you know Kou Shi?You can claim authorship or link another user.Do you know Zun Wang?You can claim authorship or link another user.Do you know Qisheng Su?You can claim authorship or link another user.Do you know Shiting Huang?You can claim authorship or link another user.Do you know Ziao Zhang?You can claim authorship or link another user.Do you know Zhen Fang?You can claim authorship or link another user.Do you know Qingnan Ren?You can claim authorship or link another user.Do you know Jin Liu?You can claim authorship or link another user.Do you know Yu Zeng?You can claim authorship or link another user.Do you know Yiming Zhao?You can claim authorship or link another user.Do you know Lin Chen?You can claim authorship or link another user.Do you know Zehui Chen?You can claim authorship or link another user.Do you know Feng Zhao?You can claim authorship or link another user.

Abstract

Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples an instruction, an initialized environment, a reference solution, and an executable verifier; if these artifacts are generated from inconsistent assumptions, the resulting task may be unsolvable or incorrectly evaluated. Meanwhile, multi-stage synthesis can discard the goals, dependencies, state transitions, and procedural constraints encoded in the original sources. We present FACET (Fine-grained Agentic Construction of Executable Tasks), a framework that addresses both information preservation and cross-artifact consistency. FACET reconstructs related agent skills into coherent, information-rich scenarios, then realizes and repairs the execution environment before generating the final task artifacts. The resulting container state serves as shared grounding for the instruction, solution, and verifier, while execution-based validation and targeted repair correct artifact-specific failures without unnecessarily regenerating valid components. FACET produces complex terminal tasks with dense executable checks, and successful trajectories collected from these tasks provide effective, data-efficient supervision. Fine-tuning models across multiple scales consistently improves performance on Terminal-Bench 2.1, while analyses of alternative generation schemes support the importance of environment-grounded construction for task validity and solution-verifier alignment. These results establish source-intent preservation and shared executable-state grounding as key principles for scalable terminal-task synthesis.

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