BRIEF: Bi-level Coreset Selection for Efficient Instruction Tuning in LLMs
Summary: BRIEF introduces bi-level coreset selection for LLM instruction tuning, decomposing SFT loss into knowledge vs. instruction-following contributions. A submodular composite-gradient objective yields bounded-approximation subset selection, cutting tuning cost ~3x while improving downstream accuracy. (summarized by gpt-5.4-mini on Apr 12 2026)
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Authors
- 1. Chaoyuan Shen (Beijing Institute of Technology)
- 2. Chi Zhang (Beijing Institute of Technology)
- 3. Chengliang Chai (Beijing Institute of Technology)
- 4. Jiacheng Wang (Beijing Institute of Technology)
- 5. Jia Yuan (University of Arizona)
- 6. Yuping Wang (Beijing Institute of Technology)
- 7. Ye Yuan (Beijing Institute of Technology)
- 8. Guoren Wang (Beijing Institute of Technology)
- 9. Lei Cao (Massachusetts Institute of Technology)
BibTeX Citation
@article{shen_vldb26,
title = {{BRIEF: Bi-level Coreset Selection for Efficient Instruction Tuning in LLMs}},
author = {Shen, Chaoyuan and Zhang, Chi and Chai, Chengliang and Wang, Jiacheng and Yuan, Jia and Wang, Yuping and Yuan, Ye and Wang, Guoren and Cao, Lei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {6},
pages = {1264--1277},
doi = {10.14778/3797919.3797933},
url = {https://doi.org/10.14778/3797919.3797933},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,221 | Camel: Managing Data for Efficient Stream Learning | 2022 | SIGMOD | 7.6271601e-05 |
| 3,886 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 7.0460597e-05 |
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