UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing
Score
78提出插入操作完善编辑空间，系统解决规则与LLM方法的矛盾，方法新颖且实验充分。
论文结构清晰，方法描述详细，实验对比全面，但缺乏对失败案例的深入分析。
直接面向大规模预训练数据精炼的实际需求，框架设计考虑效率与可靠性，具有工程落地潜力。
聚焦当前大模型训练数据瓶颈热点，论文日期接近当前时间，具有高度时效性。
来自arXiv预印本，作者为学术机构，但尚未经同行评议。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The method is highly innovative, systematically addressing the weaknesses of both rule-based and LLM-based approaches, with thorough experimental validation and significant results.
AI commentary
「This paper precisely addresses the core pain point of current LLM training—the data quality bottleneck. UltraX's innovation lies in replacing direct refinement with programmatic editing, retaining the efficiency of rule-based methods while absorbing the flexibility of LLMs. It's a highly practical framework worth attention for researchers and engineers working on pre-training data cleaning and model training.」
Quick overview
As available training data approaches its physical limit, gains from Scaling Laws diminish, shifting focus to data quality. Existing refinement methods face trade-offs: rule-based approaches are rigid, LLM-based ones inefficient. This paper proposes UltraX, a framework completing the editing function space by introducing insertion, enabling fine-grained instance-level editing. It builds a reliable program-supervision pipeline with dataset-adaptive prompt optimization, line alignment mapping, dynamic context replacement, low-confidence filtering, and ratio-controlled sampling. During inference, sliding-window prediction, global operation aggregation, and post-processing ensure stability. Experiments show UltraX achieves the highest average performance across all corpora and matches or surpasses baselines with fewer training tokens, demonstrating superior data efficiency and refinement reliability.
- UltraX completes the editing function space by introducing insertion for fine-grained instance-level editing.
- It proposes a supervision pipeline with dataset-adaptive prompt optimization, line alignment mapping, and dynamic context replacement.
- Low-confidence filtering and ratio-controlled sampling stabilize the training distribution.
- Sliding-window prediction and global operation aggregation improve reliability at scale.
Full text
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