SDO: Structure-Aware Data Organization for Efficient LLM Post-Training
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 framewor...
| Authors | Jinliang Gao, Ning Yang, Hai Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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