SDO: Structure-Aware Data Organization for Efficient LLM Post-Training

AI 中文解读

SDO框架给大模型训练装上了“智能调度员”，让AI学习效率大幅提升，这可能是降低大模型训练成本的新突破口。过去训练AI时，所有数据都被平均地喂给模型，就像老师用同样的题海战术教所有学生，学得快的浪费时间，学得慢的跟不上。SDO则聪明地根据每个数据点的相似程度分组，并跟踪哪些数据被“学”了多少次，对已经掌握的内容减少重复练习，把精力留给还没吃透的部分。效果立竿见影：在指令微调、偏好对齐等主流训练环节中，AI的收敛速度明显加快，早期到中期提升最显著，还能在不同类型的问题上表现更均衡。对普通人来说，这意味着一方面未来的AI助手能用更低成本训练出更强的能力，服务价格可能更亲民；另一方面，AI在回答问题时可能更少出现“偏科”——不会只擅长某类题目，在其他问题上掉链子。大模型训练更高效，整个AI行业的发展节奏也会因此加快。

arXiv:2607.27273v1 Announce Type: new 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.