SDO: Structure-Aware Data Organization for Efficient LLM Post-Training
A new arXiv paper proposes SDO, a plug-and-play data organization framework that adapts mini-batch composition and sample exposure using representation-space structure, improving convergence and accuracy balance in LLM post-training (SFT, DPO, GRPO) without excluding samples.
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[Submitted on 29 Jul 2026]
Title:SDO: Structure-Aware Data Organization for Efficient LLM Post-Training
View a PDF of the paper titled SDO: Structure-Aware Data Organization for Efficient LLM Post-Training, by Jinliang Gao and 4 other authors
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Abstract:Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing training schedules. However, data organization itself is usually treated as a static preprocessing step: embedding-based grouping methods construct fixed partitions before training and cannot adapt to the evolving sample exposure during optimization. As a result, all samples receive similar exposure despite their different optimization needs, leading to redundant updates for some samples while leaving others under-optimized. To address this problem, we propose SDO (Structure-Aware Data Organization), a plug-and-play data organization framework with an exposure-driven feedback mechanism that organizes mini-batch composition and sample exposure according to representation-space structure. SDO operates epoch by epoch on frozen external embeddings, avoiding model warm-up training overhead: within each epoch, locality-aware batching forms coherent mini-batches via KNN neighborhood traversal; across epochs, exposure-balanced scheduling records per-sample participation and reduces the sampling probability of over-exposed samples to preserve long-term coverage. Across SFT, DPO, and GRPO, SDO accelerates convergence, with the largest gains observed in the early-to-mid phase, producing more coherent gradients and more balanced accuracy across question types without permanently excluding training samples.
Comments: 9 pages, 5 figures, 5 tables
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.27273 [cs.LG]
(or arXiv:2607.27273v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.27273
arXiv-issued DOI via DataCite
Submission history
From: Jinliang Gao [view email] [v1] Wed, 29 Jul 2026 12:17:18 UTC (17,811 KB)
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