SETA: Scaling Environments for Terminal Agents

2026-07-12

AI Summary

This work addresses the challenge of limited large-scale, diverse, and verifiable training environments for terminal-based reinforcement learning (RL). It introduces SETA, a framework featuring two pipelines—SETA-Synth and SETA-Evol—that enable the first scalable and verifiable automatic generation of terminal RL environments. The framework incorporates a unified verification mechanism and a difficulty-adaptive evolution strategy, yielding SETA-Env, an open dataset comprising over 4,500 tasks. By integrating instruction synthesis, environment construction, and automated validation, the authors train agents using the GRPO algorithm on Qwen3-8B and DeepSeek-V4-Flash models. On Terminal-Bench 2.0, these agents achieve 12% pass@1 (state-of-the-art for 8B models) and 43% pass@1 (a 3% improvement), with pass@5 reaching 58% (a 4% gain).

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.

Problem

• terminal agents
• reinforcement learning
• environment generation
• verifiable tasks
• scalable training

Innovation

• terminal agents
• reinforcement learning
• verifiable environments
• scalable framework
• environment generation