Training data for terminal agents often ships tasks where instruction, environment, solution, and verifier disagree — producing unsolvable tasks. A new synthesis framework cuts that at the root. Title: FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis URL: 📌 Overview Reconstructs related skills into rich scenarios, builds the environment first, and grounds instruction, solution, and verifier in that same executable state. 🧩 Problems it solves ・Multi-stage generation loses source dependencies and intermediate states ・Misaligned instruction/environment/solution/verifier yield unsolvable, unverifiable tasks ⚙️ Method ・Collects 71K+ skills, reconstructs into 5-dimensional scenarios ・Builds the environment in Docker, exposing its real state as a shared channel ・Generates instruction→solution→verifier sequentially; a router pinpoints and repairs only the failing part 📊 Results ・Synthesizes 6,078 validated tasks at 22.77 tests/task on average ・Fine-tuning Qwen3.5: 4B +40.5%, 9B +30.1%, 27B +16.5% ・The 27B (47.57) nears the ~15x larger 397B (49.06) ・70% end-to-end yield vs 15–28% for competitors A clear case that high-quality executable tasks come from careful state grounding, not brute-force generation. #LLMAgents# #TerminalBench#