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Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning research paper by Alibaba (Qwen), 2025

Alibaba (Qwen) · Sep 28, 2025 · Inference and efficiency · 9 citations · 61 upvotes · unverified

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What it shows

Quadrant-based Tuning (Q-Tuning) optimizes both sample and token pruning in supervised fine-tuning of large language models, achieving superior performance with reduced data.

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

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About this paper
Authors
Shaobo Wang, Jiaming Wang, Jiajun Zhang and 8 more
arXiv
2509.23873 · PDF
Venue
arXiv.org
Citations
9, 0 influential · Semantic Scholar
Upvotes
61 · Hugging Face
Code
github.com/gszfwsb/Q-tuning
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 9Sep 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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