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MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration research paper by Microsoft, 2026

Microsoft · Feb 2, 2026 · Training and scaling · 1 citations · 34 upvotes · unverified

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What it shows

Training instability in large language models is linked to weight matrix stable rank decline and Jacobian alignment, which MSign addresses through matrix sign operations to prevent gradient explosions.

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

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About this paper
Authors
Lianhai Ren, Yucheng Ding, Xiao Liu and 3 more
arXiv
2602.01734 · PDF
Venue
arXiv.org
Citations
1, 0 influential · Semantic Scholar
Upvotes
34 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 1Sep 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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