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OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration research paper by Alibaba (Qwen), 2026

Alibaba (Qwen) · Feb 5, 2026 · Inference and efficiency · 12 citations · 274 upvotes · unverified

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What it shows

OPUS is a dynamic data selection framework that improves pre-training efficiency by scoring data candidates based on optimizer-induced update projections in a stable proxy-derived target space, achieving superior performance with reduced computational overhead.

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

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About this paper
Authors
Shaobo Wang, Xuan Ouyang, Tianyi Xu and 9 more
arXiv
2602.05400 · PDF
Venue
arXiv.org
Citations
12, 0 influential · Semantic Scholar
Upvotes
274 · Hugging Face
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 12Sep 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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