Terminal-Corpus: Large-Scale SFT Dataset for Terminal Agents

Terminal-Corpus is a large-scale Supervised Fine-Tuning (SFT) dataset designed to scale the terminal interaction capabilities of Large Language Models (LLMs). Developed by NVIDIA, this dataset was built using the Terminal-Task-Gen pipeline, which combines dataset adaptation with synthetic task generation across diverse domains.

🚀 Key Results & Performance

The high-quality trajectories in Terminal-Corpus enable models of various sizes to achieve performance that rivals or exceeds much larger frontier models on the Terminal-Bench 2.0 benchmark.

1. Overall Performance Comparison

Training on Terminal-Corpus yields substantial gains across the Qwen3 model family:

The Nemotron-Terminal-32B (27.4%) outperforms the 480B-parameter Qwen3-Coder (23.9%) and Gemini 2.5 Flash (16.9%). Nemotron-Terminal-14B (20.2%) achieves higher accuracy than the 120B GPT-OSS (high) (18.7%).

2. Domain-Specific Breakthroughs

The dataset unlocks functional utility in complex domains where base models previously showed near-zero capability:

📂 Dataset Composition

The released dataset contains approximately 366k high-quality execution trajectories split into two major streams:

• Dataset Adapters (~226k samples): Transformations of high-quality Math, Code, and Software Engineering (SWE) datasets into terminal-based formats.
• Skill-based Synthetic Tasks (~140k samples): Novel tasks generated from a structured taxonomy of primitive terminal skills.

📜 Citation

If you use this dataset in your research, please cite the following work:

@misc{pi2026dataengineeringscalingllm,
      title={On Data Engineering for Scaling LLM Terminal Capabilities}, 
      author={Renjie Pi and Grace Lam and Mohammad Shoeybi and Pooya Jannaty and Bryan Catanzaro and Wei Ping},
      year={2026},
      eprint={2602.21193},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.21193}, 
}