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On Data Engineering for Scaling LLM Terminal Capabilities research paper by NVIDIA, 2026

NVIDIA · Feb 24, 2026 · Training and scaling · 26 citations · 104 upvotes · unverified

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What it shows

Researchers developed a synthetic task generation pipeline and analyzed data strategies to improve terminal agent performance, creating a large-scale dataset and models that outperform larger counterparts on benchmark tests.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Renjie Pi, Grace Lam, Mohammad Shoeybi and 3 more
arXiv
2602.21193 · PDF
Venue
arXiv.org
Citations
26, 8 influential · Semantic Scholar
Upvotes
104 · Hugging Face
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 8Sep 25, 2026
Citationsfirst count: 26Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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