Title: RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models, Pretraining Data Refinement

Abstract: The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pretraining corpora. However, enhancing data quality at scale remains a significant challenge, primarily due to the trade-off between refinement effectiveness and processing efficiency.
While rule-based filtering remains the dominant paradigm, it typically operates at the document level and lacks the granularity needed to refine specific content within documents.
Inspired by emerging work such as ProX, we propose **RefineX**, a novel framework for large-scale, surgical refinement of pretraining data through programmatic editing tasks. RefineX enables efficient and fine-grained data refinement while reliably preserving the diversity and naturalness of raw text.
The core strength of RefineX lies in its ability to distill high-quality, expert-guided end-to-end refinement results into minimal edit-based deletion programs.
This high-precision distillation pipeline is used to train an efficient and reliable refine model that can systematically improve every instance in the corpus at scale.
We evaluate RefineX across from-scratch pretraining at multiple model scales and find that it consistently outperforms models trained on raw, filtered, or alternatively refined data across diverse downstream tasks. On the 750M model, RefineX yields 2.6\%-7.2\% average gains on lighteval tasks, and achieves comparable performance using significantly fewer training tokens.
Further analysis shows that RefineX reliably enhances text quality with both high efficiency and precision, outperforming prior approaches such as end-to-end generation and Prox-C. These results position RefineX as a scalable, effective, and reliable solution for optimizing pretraining data in modern LLM pipelines.