# Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models

arXiv: https://arxiv.org/abs/2506.04689 · HTML: https://mlsanity.com/paper/2506.04689/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 45 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 30 of 39)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://mlsanity.com/paper/2402.00159/) (2024)
- [TinyStories: How Small Can Language Models Be and Still Speak Coherent English?](https://mlsanity.com/paper/2305.07759/) (2023)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [DataComp-LM: In search of the next generation of training sets for language models](https://mlsanity.com/paper/2406.11794/) (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)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [RedPajama: an Open Dataset for Training Large Language Models](https://mlsanity.com/paper/2411.12372/) (2024)
- [A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity](https://mlsanity.com/paper/2305.13169/) (2023)
- [Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://mlsanity.com/paper/2412.02595/) (2024)
- [Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://mlsanity.com/paper/2401.16380/) (2024)
- [QuRating: Selecting High-Quality Data for Training Language Models](https://mlsanity.com/paper/2402.09739/) (2024)
- [Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data](https://mlsanity.com/paper/2404.01413/) (2024)

## Built on by (most cited first, 30 of 33)

- [CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks](https://mlsanity.com/paper/2507.23751/) (2025)
- [BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining](https://mlsanity.com/paper/2508.10975/) (2025)
- [Data-efficient pre-training by scaling synthetic megadocs](https://mlsanity.com/paper/2603.18534/) (2026)
- [Synthetic bootstrapped pretraining](https://mlsanity.com/paper/2509.15248/) (2025)
- [RePro: Training Language Models to Faithfully Recycle the Web for Pretraining](https://mlsanity.com/paper/2510.10681/) (2025)
- [Self-Improving Pretraining: using post-trained models to pretrain better models](https://mlsanity.com/paper/2601.21343/) (2026)
- [How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data](https://mlsanity.com/paper/2604.13977/) (2026)
- [Thinking Augmented Pre-training](https://mlsanity.com/paper/2509.20186/) (2025)
- [Motif 2 12.7B technical report](https://mlsanity.com/paper/2511.07464/) (2025)
- [Scale Dependent Data Duplication](https://mlsanity.com/paper/2603.06603/) (2026)
- [Toward Cybersecurity-Expert Small Language Models](https://mlsanity.com/paper/2510.14113/) (2025)
- [Large-Scale Diverse Synthesis for Mid-Training](https://mlsanity.com/paper/2508.01326/) (2025)
- [Data-Centric Lessons To Improve Speech-Language Pretraining](https://mlsanity.com/paper/2510.20860/) (2025)
- [Data Darwinism Part II: DataEvolve -- AI can Autonomously Evolve Pretraining Data Curation](https://mlsanity.com/paper/2603.14420/) (2026)
- [How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline](https://mlsanity.com/paper/2604.04204/) (2026)
- [Induction Signatures Are Not Enough: A Matched-Compute Study of Load-Bearing Structure in In-Context Learning](https://mlsanity.com/paper/2509.22947/) (2025)
- [Concept-Aware Batch Sampling Improves Language-Image Pretraining](https://mlsanity.com/paper/2511.20643/) (2025)
- [DynaMoE: Dynamic Token-Level Expert Activation with Layer-Wise Adaptive Capacity for Mixture-of-Experts Neural Networks](https://mlsanity.com/paper/2603.01697/) (2026)
- [Generating Pretraining Tokens from Organic Data for Data-Bound Scaling](https://mlsanity.com/paper/2605.17849/) (2026)
- [Building a Strong Instruction Language Model for a Less-Resourced Language](https://mlsanity.com/paper/2603.01691/) (2026)
- [DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data](https://mlsanity.com/paper/2607.24717/) (2026)
- [Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining](https://mlsanity.com/paper/2509.24356/) (2025)
- [Improving Romanian LLM Pretraining Data using Diversity and Quality Filtering](https://mlsanity.com/paper/2511.01090/) (2025)
- [FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale](https://mlsanity.com/paper/2601.22146/) (2026)
- [Language Modeling and Understanding Through Paraphrase Generation and Detection](https://mlsanity.com/paper/2602.08274/) (2026)
- [Continually self-improving AI](https://mlsanity.com/paper/2603.18073/) (2026)
- [Synthetic Rewriting as a Quality Multiplier: Evidence from Portuguese Continued Pretraining](https://mlsanity.com/paper/2603.24826/) (2026)
- [WRAP++: Web discoveRy Amplified Pretraining](https://mlsanity.com/paper/2604.06829/) (2026)
- [Alignment Plausibility: A New Standard for Assuring AI in Healthcare](https://mlsanity.com/paper/2607.07766/) (2026)
- [Blind Men and the Elephant: Probing the Epistemic Myopia of LLMs under Long-Tail Divergent Knowledge](https://mlsanity.com/paper/2608.28478/) (2026)

## Abstract (arXiv)

Scaling laws predict that the performance of large language models improves with increasing model size and data size. In practice, pre-training has been relying on massive web crawls, using almost all data sources publicly available on the internet so far. However, this pool of natural data does not grow at the same rate as the compute supply. Furthermore, the availability of high-quality texts is even more limited: data filtering pipelines often remove up to 99% of the initial web scrapes to achieve state-of-the-art. To address the "data wall" of pre-training scaling, our work explores ways to transform and recycle data discarded in existing filtering processes. We propose REWIRE, REcycling the Web with guIded REwrite, a method to enrich low-quality documents so that they could become useful for training. This in turn allows us to increase the representation of synthetic data in the final pre-training set. Experiments at 1B, 3B and 7B scales of the DCLM benchmark show that mixing high-quality raw texts and our rewritten texts lead to 1.0, 1.3 and 2.5 percentage points improvement respectively across 22 diverse tasks, compared to training on only filtered web data. Training on the raw-synthetic data mix is also more effective than having access to 2x web data. Through further analysis, we demonstrate that about 82% of the mixed in texts come from transforming lower-quality documents that would otherwise be discarded. REWIRE also outperforms related approaches of generating synthetic data, including Wikipedia-style paraphrasing, question-answer synthesizing and knowledge extraction. These results suggest that recycling web texts holds the potential for being a simple and effective approach for scaling pre-training data. We make our high-quality synthetic data publicly available at https://huggingface.co/datasets/facebook/recycling_the_web.