[Paper] On Data Engineering for Scaling LLM Terminal Capabilities

Source: arXiv - 2602.21193v1

Overview

Large language models (LLMs) are getting better at acting as “terminal agents” – they can issue shell commands, manipulate files, and automate workflows. However, the data pipelines that make these capabilities possible have been largely hidden. This paper demystifies the process by introducing a lightweight synthetic‑task generator and a thorough analysis of data‑engineering tricks that dramatically boost terminal‑task performance, even for modest‑size models.

Key Contributions

• Terminal‑Task‑Gen: an open‑source pipeline that creates synthetic terminal tasks from simple seed prompts or skill templates, enabling rapid dataset expansion without manual labeling.
• Terminal‑Corpus: a large, publicly released dataset (≈ tens of billions of token‑level examples) built with Terminal‑Task‑Gen, covering a wide range of command‑line operations.
• Systematic study of training tricks: evaluation of data filtering, curriculum learning, long‑context fine‑tuning, and scaling laws specific to terminal tasks.
• Nemotron‑Terminal family: three models (8B, 14B, 32B) fine‑tuned on Terminal‑Corpus that close the gap to much larger proprietary agents, achieving up to a 27 % success rate on the challenging Terminal‑Bench 2.0 benchmark.
• Open‑source release: model checkpoints, the synthetic data generator, and most of the generated data are made available on Hugging Face for the community.

Methodology

1. Synthetic Task Generation

   • Seed‑based mode: start from a small set of human‑written command‑execution examples; the generator mutates them (e.g., changing file names, parameters) to produce diverse variants.
   • Skill‑based mode: define high‑level “skills” (file navigation, process management, package installation, etc.) and let the system automatically compose multi‑step tasks that exercise those skills.
   • The pipeline outputs paired data: a natural‑language instruction and the exact terminal transcript (commands + outputs).

2. Dataset Curation

   • Apply heuristic filters (e.g., remove commands that require privileged access, filter out nonsensical outputs).
   • Balance the corpus across skill categories to avoid over‑fitting to a narrow set of operations.

3. Training Strategies

   • Curriculum Learning: start training on short, single‑step tasks, then gradually introduce longer, multi‑step sequences.
   • Long‑Context Fine‑Tuning: extend the context window (up to 32 k tokens) so the model can see the full command history when solving complex tasks.
   • Scaling Experiments: compare the same training recipe on 8B, 14B, and 32B base models (Qwen‑3) to understand how performance scales with model size.

4. Evaluation

   • Use Terminal‑Bench 2.0, a benchmark of 1 000+ real‑world command‑line problems covering diverse domains (system admin, data processing, dev‑ops).
   • Measure success as the percentage of tasks where the model’s generated command sequence exactly reproduces the ground‑truth execution trace.

Results & Findings

• Curriculum learning contributed ~3–4 pp gains across sizes, especially for longer tasks.
• Long‑context windows were crucial for the 32B model, adding another ~5 pp improvement on multi‑step benchmarks.
• Scaling behaved sub‑linearly: the 32B model did not double the 14B performance, but the gap to much larger proprietary agents (e.g., 70B‑scale) narrowed dramatically.
• The synthetic data alone (without any human‑curated terminal examples) was sufficient to achieve these gains, confirming the efficacy of the generation pipeline.

Practical Implications

• Rapid Prototyping of CLI Assistants: Developers can now bootstrap a terminal‑capable assistant with a few hundred seed examples instead of labor‑intensive data collection.
• Cost‑Effective Deployment: An 8B‑parameter model fine‑tuned with Terminal‑Corpus reaches performance comparable to much larger, closed‑source agents, reducing inference cost and latency for on‑premise tooling.
• Custom Skill Injection: Teams can define new “skills” (e.g., Kubernetes management, cloud CLI) and automatically generate a tailored dataset, enabling domain‑specific terminal bots without extensive annotation.
• Improved DevOps Automation: Integrated into IDE extensions or CI pipelines, these models can suggest, validate, and even execute safe command sequences, cutting down manual scripting time.
• Research Acceleration: Open‑source checkpoints and data lower the barrier for academic and industry groups to explore safety, interpretability, and alignment of terminal agents.

Limitations & Future Work

• Safety Filters: The current pipeline removes privileged commands, but more sophisticated safety checks (e.g., sandboxed execution verification) are needed before production use.
• Generalization to Unseen Tools: Performance drops when encountering rarely‑used or newly released CLI utilities not represented in the synthetic corpus.
• Evaluation Scope: Terminal‑Bench 2.0 focuses on deterministic command execution; handling nondeterministic or interactive programs (e.g., editors) remains an open challenge.
• Long‑Context Overhead: Extending context windows increases memory consumption, which may limit deployment on edge devices.
• Future Directions: The authors suggest expanding the generator to incorporate real‑world command logs, exploring reinforcement‑learning‑from‑human‑feedback for safety, and studying multi‑modal extensions (e.g., combining terminal output with file‑system screenshots).

Authors

• Renjie Pi
• Grace Lam
• Mohammad Shoeybi
• Pooya Jannaty
• Bryan Catanzaro
• Wei Ping

Paper Information

• arXiv ID: 2602.21193v1
• Categories: cs.CL
• Published: February 24, 2026
• PDF: Download PDF