AI 自己造题考自己，成本降到 5 分钱
训练一个能自己操作电脑的 AI，最贵的是训练数据：每个任务要人工保证指令、环境、答案、评分标准四者一致，一个任务要花几百到几千美元。这篇论文让 AI 自己造题：从少量人工验证过的任务出发，AI 把答案改长、把评分标准和指令同步更新、在全新环境里验证，通过的题再当种子继续造。15 轮下来造了 3.7 万道题，单题成本约 5 分钱，难度还一路飙升——AI 的通过率从 90% 跌到 2.5%。用这些题微调模型，在三个终端操作基准上最高涨了 10 个点。它不是你明天能用上的，但这是 AI 自己训练自己的关键一步：数据瓶颈可能被递归合成打破。
📄 原文摘要(英文)
High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent. Human authoring does not scale, and direct generation with large language models (LLMs) often breaks these dependencies. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon terminal-agent tasks at scale. Starting from verified seed tasks, RST extends the reference solution, realigns the verifier and instruction to the new workflow, validates the result in a fresh sandbox, and reuses accepted tasks as seeds for subsequent rounds. Across fifteen recursive rounds, RST produces 37,484 synthesized terminal-agent tasks at roughly \0.05 per task. Task difficulty increases substantially over rounds: the median reference solution grows from 67 to 374 lines, the median number of executed commands grows from 40 to 244, and DeepSeek-V4-Pro pass@4 drops from 90% at R_1 to 2.5% at R_{15}$. To demonstrate training utility, we collect rejection-sampled Qwen3.5 trajectories on the synthesized tasks and use them for supervised fine-tuning. Fine-tuning on these trajectories improves Qwen3.5-27B and Qwen3.5-122B-A10B by up to 10 points on Terminal-Bench~2, Terminal-Bench Hard, and Long-Horizon Terminal Bench, while agentic PPO lifts Qwen3.5-27B to 49.44\%, 32.00\%, and 22.07\% on the three benchmarks, corresponding to relative gains of 20.0\%, 41.2\%, and 21.9\% over the base model. Moreover, after 15 rounds, the recursion shows no ceiling: synthesis yield and validation rates remain stable as difficulty keeps climbing, indicating that the process can continue well beyond the scale reported here.