{"ID":6536281,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10891","arxiv_id":"2607.10891","title":"SETA: Scaling Environments for Terminal Agents","abstract":"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.","short_abstract":"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 lear...","url_abs":"https://arxiv.org/abs/2607.10891","url_pdf":"https://arxiv.org/pdf/2607.10891v1","authors":"[\"Qijia Shen\",\"Zhiqi Huang\",\"Vamsidhar Kamanuru\",\"Aznaur Aliev\",\"Jay Rainton\",\"Ahmed Awelkair\",\"Zhichen Zeng\",\"Jiajun Li\",\"Shi Dong\",\"Yueming Yuan\",\"Boyuan Ma\",\"Qizheng Zhang\",\"Jiwei Fu\",\"Yuzhen Mao\",\"Wendong Fan\",\"Ping Nie\",\"Philip Torr\",\"Bernard Ghanem\",\"Changran Hu\",\"Jonathan Lingjie Li\",\"Urmish Thakker\",\"Guohao Li\"]","published":"2026-07-12T19:40:27Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\",\"Language Model\"]","has_code":false}