这篇论文提出了DataOrchestra，能针对每个数据样本定制清洗流程，在多个模型和基准上都有提升，还节省算力。
DataOrchestra提出针对每个数据样本定制处理流程，统一丢弃、保留或清洗操作。清洗时选择程序化编辑或LLM重写，并生成具体指令。在0.5B至7B模型上从零预训练，11个基准平均提升优于单个处理方法。数学持续预训练中超过更强基线，且跳过不必要操作减少计算量。
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
Pretraining data processing is critical to the downstream performance of Large Language Models (LLMs). However, many existing approaches define a fixed processing strategy at the corpus or domain level and apply it uniformly to many examples, without adapting to the needs of each example. We propose DataOrchestra, a framework that unifies different processing operations and orchestrates an example-specific pipeline for each example. Given a chunk of pretraining data, an orchestrator decides whether to drop, untouch, or clean it. For a chunk to be cleaned, it selects one or more downstream operations, ranging from programmatic editing to different forms of LLM-based rewriting. For each rewriting step, it further generates a concrete instruction, which is executed by the corresponding downstream tool model. We pretrain models from 0.5B to 7B from scratch on web data processed by DataOrchestra and observe stable average gains over individual data-processing methods across 11 benchmarks. DataOrchestra is also effective for math continued pretraining and outperforms stronger processing baselines, while reducing processing compute by skipping unnecessary downstream operations.