终端代理训练数据难找，SETA开源了4500+环境的RL数据集，Qwen3-8B训后Terminal-Bench拿到12%，顺带还让DeepSeek-V4涨了分，搞终端AI的别错过。
SETA是一个可扩展框架，用于生成可验证的终端环境以支持强化学习。其构建的SETA-Env数据集包含超过4500个环境，是当前最大的开源可验证终端RL数据集。使用Qwen3-8B在SETA-Env上训练，在Terminal-Bench 2.0上达到12% pass rate，为8B规模RL训练模型的最佳结果。在DeepSeek-V4-Flash上，同一终端代理基准的pass@1从40%提升至43%，pass@5从54%提升至58%。SETA-Env为终端代理研究提供了高质量的训练环境。
SETA: Scaling Environments for Terminal Agents
Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among these, the terminal command line provides a text-based, general-purpose interface, covering tasks from system operations to data science and machine learning. However, scaling terminal-agent training remains challenging, as it requires diverse and coherent task instructions, executable environments, and reliable verification, while lacking naturally grounded supervision data. In this work, we propose SETA, a scalable framework for generating verifiable terminal environments for reinforcement learning (RL). The framework consists of two pipelines sharing a unified verification mechanism: SETA-Synth converts diverse sources into standardized RL environments, and SETA-Evol further expands from existing environments with adaptive control of difficulty and diversity. Together, we construct and release SETA-Env, the largest open-source verifiable terminal RL dataset to date, containing over 4,500 environments. We evaluate our dataset by training Qwen3-8B with GRPO on SETA-Env, achieving 12% pass rate on Terminal-Bench 2.0, the best reported result for an RL-trained model at the 8B scale. We further observe gains on DeepSeek-V4-Flash under the same terminal agent harness, with pass@1 on Terminal-Bench 2.0 improving from 40% to 43% and pass@5 improving from 54% to 58%. These results demonstrate that SETA- Env provides high-quality training environments for terminal agents and serves as a valuable resource for advancing research on terminal-based agent learning.