On Data Engineering for Scaling LLM Terminal Capabilities

• URL: http://arxiv.org/abs/2602.21193v1
• Date: Tue, 24 Feb 2026 18:51:04 GMT
• Title: On Data Engineering for Scaling LLM Terminal Capabilities
• Authors: Renjie Pi, Grace Lam, Mohammad Shoeybi, Pooya Jannaty, Bryan Catanzaro, Wei Ping,
• Abstract summary: Training data strategies behind state-of-the-art terminal agents remain largely undisclosed.<n>We address this gap through a systematic study of data engineering practices for terminal agents.<n>Our pipeline yields Terminal-Corpus, a large-scale open-source dataset for terminal tasks.
• Score: 62.14352406328365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Nemotron-Terminal, a family of models initialized from Qwen3(8B, 14B, 32B) that achieve substantial gains on Terminal-Bench 2.0: Nemotron-Terminal-8B improves from 2.5% to 13.0% Nemotron-Terminal-14B improves from 4.0% to 20.2%, and Nemotron-Terminal-32B improves from 3.4% to 27.4%, matching the performance of significantly larger models. To accelerate research in this domain, we open-source our model checkpoints and most of our synthetic datasets at https://huggingface.co/collections/nvidia/nemotron-terminal.

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