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BitNet Distillation research paper by Microsoft, 2025

Microsoft · Oct 15, 2025 · Training and scaling · 2 citations · 62 upvotes · unverified

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What it shows

BitNet Distillation fine-tunes large language models to 1.58-bit precision using SubLN, multi-head attention distillation, and continual pre-training, achieving comparable performance with significant memory and inference speed improvements.

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

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About this paper
Authors
Xun Wu, Shaohan Huang, Wenhui Wang and 4 more
arXiv
2510.13998 · PDF
Venue
arXiv.org
Citations
2, 0 influential · Semantic Scholar
Upvotes
62 · 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: 2Sep 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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