# Organize the Web: Constructing Domains Enhances Pre-Training Data Curation

arXiv: https://arxiv.org/abs/2502.10341 · HTML: https://mlsanity.com/paper/2502.10341/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 88 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 30 of 70)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://mlsanity.com/paper/2402.00159/) (2024)
- [Scaling Data-Constrained Language Models](https://mlsanity.com/paper/2305.16264/) (2023)
- [DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining](https://mlsanity.com/paper/2305.10429/) (2023)
- [DataComp-LM: In search of the next generation of training sets for language models](https://mlsanity.com/paper/2406.11794/) (2024)
- [2 OLMo 2 Furious](https://mlsanity.com/paper/2501.00656/) (2024)

## Built on by (most cited first, 30 of 76)

- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [OpenThoughts: Data Recipes for Reasoning Models](https://mlsanity.com/paper/2506.04178/) (2025)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://mlsanity.com/paper/2502.18443/) (2025)
- [Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training](https://mlsanity.com/paper/2504.13161/) (2025)
- [Learning to Reason Across Parallel Samples for LLM Reasoning](https://mlsanity.com/paper/2506.09014/) (2025)
- [Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models](https://mlsanity.com/paper/2506.04689/) (2025)
- [Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation](https://mlsanity.com/paper/2508.13144/) (2025)
- [BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining](https://mlsanity.com/paper/2508.10975/) (2025)
- [OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration](https://mlsanity.com/paper/2602.05400/) (2026)
- [Language Models Improve When Pretraining Data Matches Target Tasks](https://mlsanity.com/paper/2507.12466/) (2025)
- [FlexOlmo: Open Language Models for Flexible Data Use](https://mlsanity.com/paper/2507.07024/) (2025)
- [How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining](https://mlsanity.com/paper/2511.18903/) (2025)
- [DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation](https://mlsanity.com/paper/2601.09688/) (2026)
- [Group-Level Data Selection for Efficient Pretraining](https://mlsanity.com/paper/2502.14709/) (2025)
- [DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation](https://mlsanity.com/paper/2512.17776/) (2025)
- [AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages](https://mlsanity.com/paper/2601.06395/) (2026)
- [GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning](https://mlsanity.com/paper/2602.21492/) (2026)
- [LLM Fingerprinting via Semantically Conditioned Watermarks](https://mlsanity.com/paper/2505.16723/) (2025)
- [Why Do Some Inputs Break Low-Bit LLM Quantization?](https://mlsanity.com/paper/2506.12044/) (2025)
- [Gaperon: A Peppered English-French Generative Language Model Suite](https://mlsanity.com/paper/2510.25771/) (2025)
- [Essential-Web v1.0: 24T tokens of organized web data](https://mlsanity.com/paper/2506.14111/) (2025)
- [Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM](https://mlsanity.com/paper/2511.23119/) (2025)
- [TiKMiX: Take Data Influence into Dynamic Mixture for Language Model Pre-training](https://mlsanity.com/paper/2508.17677/) (2025)
- [AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection](https://mlsanity.com/paper/2505.07293/) (2025)
- [Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training](https://mlsanity.com/paper/2507.15640/) (2025)
- [Joint Selection for Large-Scale Pre-Training Data via Policy Gradient-based Mask Learning](https://mlsanity.com/paper/2512.24265/) (2025)
- [Topic Over Source: The Key to Effective Data Mixing for Language Models Pre-training](https://mlsanity.com/paper/2502.16802/) (2025)
- [Semantic Probabilistic Control of Language Models](https://mlsanity.com/paper/2505.01954/) (2025)
- [OLMoASR: Open Models and Data for Training Robust Speech Recognition Models](https://mlsanity.com/paper/2508.20869/) (2025)

## Abstract (arXiv)

Modern language models are trained on large, unstructured datasets consisting of trillions of tokens and obtained by crawling the web. The unstructured nature makes it difficult to reason about their contents and develop systematic approaches to data curation. In this paper, we unpack monolithic web corpora by developing taxonomies of their contents and organizing them into domains. We introduce WebOrganizer, a framework for organizing web pages in terms of both their topic and format. Using these two complementary notions of domains, we automatically annotate pre-training data by distilling annotations from a large language model into efficient classifiers. This allows us to study how data from different domains should be mixed to improve models on downstream tasks, and we show that we can combine insights about effective topics and formats to further boost performance. We demonstrate that our domain mixing also improves existing methods that select data based on quality. Furthermore, we study and compare how quality-based methods will implicitly change the domain mixture. Overall, our work demonstrates that constructing and mixing domains provides a valuable complement to quality-based data curation methods, opening new avenues for effective and insightful pre-training data curation.