GEM: Geometric Entropy Mixing for Optimal LLM Data Curation
The paper introduces GEM, a framework designed for optimal data curation in large language models (LLMs). It addresses issues in data mixing caused by flawed categorization and proposes a new approach using geometric entropy. Experimental results indicate that GEM improves downstream accuracy and establishes a new state-of-the-art in data mixing strategies.
- ▪GEM reformulates data curation as a variational problem on the hypersphere.
- ▪The framework employs a mixing-balance regularizer to counteract cluster collapse.
- ▪Experiments show that GEM improves average downstream accuracy by up to 1.2% when integrated into existing mixing strategies.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26121 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.26121 (cs) [Submitted on 27 Apr 2026] Title:GEM: Geometric Entropy Mixing for Optimal LLM Data Curation Authors:Yue Min, Ziyun Qiao, Ruining Chen, Yujun Li View a PDF of the paper titled GEM: Geometric Entropy Mixing for Optimal LLM Data Curation, by Yue Min and 3 other authors View PDF HTML (experimental) Abstract:LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, optimal mixing is hindered by categorization flaws: human taxonomies suffer from ontological misalignment, and Euclidean clustering fails to address embedding anisotropy.
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