DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
Score
76提出逐样本动态数据编排新思路，突破固定策略局限，信息增量显著。
方法设计清晰，实验覆盖多规模模型与基准，但缺少对编排器学习过程的深入分析。
直接降低数据预处理算力消耗并提升模型性能，对工业级训练有实际参考价值。
2025年7月发布的论文，紧贴当前大模型数据工程热点，时效性很强。
来源为arXiv预印本，未经同行评审，但作者机构与实验设计较为可信。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper addresses a key bottleneck in data preprocessing with a novel approach, supported by solid experiments and significant results, offering high academic value.
AI commentary
「What excites me most about this paper is that it reduces the granularity of data processing from corpus level to per-example level and uses learning to make decisions, aligning well with the current trend of 'quality over quantity'. For research and engineering teams optimizing pretraining pipelines, DataOrchestra offers a plug-and-play candidate, especially for format-sensitive domains like math and code. However, the training cost and generalization of the orchestrator itself still need further validation.」
Quick overview
Pretraining data processing is critical for downstream performance of large language models, but existing methods often apply fixed strategies at corpus or domain level without adapting to per-example needs. This paper proposes DataOrchestra, a framework that unifies multiple processing operations and dynamically orchestrates an example-specific pipeline: an orchestrator decides to drop, keep, or clean each chunk; for cleaning, it selects programmatic editing or LLM-based rewriting with concrete instructions. Experiments from 0.5B to 7B models show stable average gains over individual methods across 11 benchmarks, outperforming stronger baselines in math continued pretraining while reducing compute by skipping unnecessary operations.
- DataOrchestra dynamically orchestrates per-example decisions (drop, keep, or clean) for pretraining data, avoiding one-size-fits-all strategies.
- The framework supports programmatic editing and various LLM-based rewriting with automatically generated instructions executed by tool models.
- Models from 0.5B to 7B show stable average gains over individual processing methods across 11 benchmarks, with larger gains in math continued pretraining.
- Processing compute is reduced by skipping unnecessary downstream operations without sacrificing performance.
Full text
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