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
- 2. Chi Zhang
- 3. Chengliang Chai
- 4. Jiacheng Wang
- 5. Jia Yuan
- 6. Yuping Wang
- 7. Ye Yuan
- 8. Guoren Wang
- 9. Lei Cao
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 2,643 | Camel: Managing Data for Efficient Stream Learning | 2022 | SIGMOD | 8.384956e-05 |
| 4,102 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 6.4522929e-05 |
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