These results establish source-intent preservation and shared executable-state grounding as key principles for scalable terminal-task synthesis as key principles for scalable terminal-task synthesis.
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.
Meta-Task is proposed, a framework that redefines terminal task synthesis as a Terminal-Bench-format task itself: an agent operates within a real container environment to iteratively generate, execute, and verify tasks, so that synthesized components are checked for internal consistency and executability within the gen...
Zhi-Hong Pan, Ji-Yuan He, Kai Zhang et al.· arXiv.org· 1 citation
These results show that verified synthetic environments can serve as a scalable and reliable training substrate for compact web agents, shifting synthetic webagent learning from surface-level plausibility toward executable, state-grounded supervision.
Cheng-Hao Zhang, Yu-Xiang Cheng, Sai-Sai Hu et al.· 0 citations
Results show that execution evidence can expose behavioral failures missed by artifact inspection and can guide Skill generation toward jointly verified functional and safety outcomes.
Zhi-Bo Zhang, Zheng-Mao Ouyang, Ling Shi et al.· 0 citations
SciConsolidate is introduced, which contrasts verified successes and failures to induce cross-task procedures, selects them through a development-validation gate, and uses failure-informed, answer-free query synthesis to expand the consolidation data without requiring pre-existing reference answers.
Liwei Dong, Jiahao Zhao, Nan Xu· arXiv.org· 0 citations
This work presents Change2Task, a system grounded in repository history that converts merged pull requests into verified tasks on healthy modern revisions of the same repository, and provides executable data for coding agent training and evaluation while reducing repeated environment setup, storage, and task constructi...
Recursive Synthetic Terminal Tasks (RST) is presented, a recursive verified synthesis framework for constructing long-horizon terminal-agent tasks at scale and shows no ceiling, indicating that the process can continue well beyond the scale reported here.
Zhongzhi Li, Yucheng Shi, Zongxia Li et al.· 5 citations
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