AI 终端任务合成：先修好环境，再出题
训练终端 AI 需要大量「指令+环境+答案+验证器」配对的任务，但现有方法常把这几样东西从不同源头拼凑，导致任务无解或评分错。FACET 的做法反直觉：先让 AI 把相关技能重构为完整场景，然后修复执行环境，再基于这个修好的环境统一生成指令、答案和验证器——所有部件共享同一个「环境状态」，保证它们说的是同一件事。实验证明，用这套方法合成的任务训练小模型，在 Terminal-Bench 2.1 上的表现持续提升。它不是你明天能用上的，但揭示了一个关键原则：给 AI 出题前，先确保出题人自己没搞错前提。
📄 原文摘要(英文)
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.