SETA (Scaling Environments for Terminal Agents) is a framework for generating verifiable terminal environments for reinforcement learning (RL). It consists of two pipelines and a large open-source dataset, SETA-Env, containing over 4,500 environments. SETA was used to train Qwen3-8B and DeepSeek-V4-Flash, achieving state-of-the-art results on Terminal-Bench 2.0.