On Data Engineering for Scaling LLM Terminal Capabilities

Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed. We address this gap through a systematic study of data engineering practices for terminal agents, making two key contributions: (1) Terminal-Task-Gen, a lightweight synthetic task generation pipeline that supports seed-based and skill-based task construction, and (2) a comprehensive analysis of data and training strategies, including filtering, curriculum learning, long context training, and scaling behavior. Our pipeline yields Terminal-Corpus, a large-scale open-source dataset for terminal tasks. Using this dataset, we train Terminal-LM, a family of models initialized from Qwen3 (8B, 14B, 32B) that achieve substantial gains on Terminal-Bench 2.0: Terminal-LM-8B improves from 2.5% to 13.0%, Terminal-LM-14B improves from 4.0% to 20.2%, and Terminal-LM-32B improves from 3.4% to 27.4%, matching the performance of significantly larger models. We release Terminal-LM checkpoints and Terminal-Corpus to accelerate research in this domain.