BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining

• URL: http://arxiv.org/abs/2508.10975v2
• Date: Tue, 19 Aug 2025 17:56:36 GMT
• Title: BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining
• Authors: DatologyAI, :, Pratyush Maini, Vineeth Dorna, Parth Doshi, Aldo Carranza, Fan Pan, Jack Urbanek, Paul Burstein, Alex Fang, Alvin Deng, Amro Abbas, Brett Larsen, Cody Blakeney, Charvi Bannur, Christina Baek, Darren Teh, David Schwab, Haakon Mongstad, Haoli Yin, Josh Wills, Kaleigh Mentzer, Luke Merrick, Ricardo Monti, Rishabh Adiga, Siddharth Joshi, Spandan Das, Zhengping Wang, Bogdan Gaza, Ari Morcos, Matthew Leavitt,
• Abstract summary: We introduce BeyondWeb, a synthetic data generation framework that produces high-quality synthetic data for pretraining.<n>BeyondWeb significantly extends the capabilities of traditional web-scale datasets.<n>It delivers up to 7.7x faster training than open web data and 2.7x faster than Nemotron- Synth.
• Score: 12.935276089987662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, the use of synthetic data for pretraining has emerged as a promising paradigm for pushing the frontier of performance. Despite this, the factors affecting synthetic data quality remain poorly understood. In this work, we introduce BeyondWeb, a synthetic data generation framework that produces high-quality synthetic data for pretraining. BeyondWeb significantly extends the capabilities of traditional web-scale datasets, outperforming state-of-the-art synthetic pretraining datasets such as Cosmopedia and Nemotron-CC's high-quality synthetic subset (Nemotron-Synth) by up to 5.1 percentage points (pp) and 2.6pp, respectively, when averaged across a suite of 14 benchmark evaluations. It delivers up to 7.7x faster training than open web data and 2.7x faster than Nemotron-Synth. Remarkably, a 3B model trained for 180B tokens on BeyondWeb outperforms an 8B model trained for the same token budget on Cosmopedia. We also present several insights from BeyondWeb on synthetic data for pretraining: what drives its benefits, which data to rephrase and how, and the impact of model size and family on data quality. Overall, our work shows that there's no silver bullet for generating high-quality synthetic pretraining data. The best outcomes require jointly optimizing many factors, a challenging task that requires rigorous science and practical expertise. Naive approaches can yield modest improvements, potentially at great cost, while well-executed methods can yield transformative improvements, as exemplified by BeyondWeb.

Related papers

• Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls [25.294408301653576]
  Training data plays a crucial role in Large Language Models (LLM) scaling, yet high quality data is of limited supply.<n>We compare natural web data, diverse synthetic types (rephrased text, generated textbooks), and mixtures of natural and synthetic data.<n>We find pre-training on rephrased synthetic data textitalone is not faster than pre-training on natural web texts.
  arXiv  Detail & Related papers  (2025-10-02T03:24:42Z)
• Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data [53.040873127309766]
  We propose a token disentanglement process within the transformer architecture, enhancing feature separation and ensuring more effective learning.<n>Our method outperforms existing models on both in-dataset and cross-dataset evaluations.
  arXiv  Detail & Related papers  (2025-09-08T17:58:06Z)
• Understanding the Influence of Synthetic Data for Text Embedders [52.04771455432998]
  We first reproduce and publicly release the synthetic data proposed by Wang et al.<n>We critically examine where exactly synthetic data improves model generalization.<n>Our findings highlight the limitations of current synthetic data approaches for building general-purpose embedders.
  arXiv  Detail & Related papers  (2025-09-07T19:28:52Z)
• Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models [92.85086256871027]
  We propose REWIRE, REcycling the Web with guIded REwrite, to enrich low-quality documents so that they could become useful for training.<n>We demonstrate that about 82% of the mixed in texts come from transforming lower-quality documents that would otherwise be discarded.
  arXiv  Detail & Related papers  (2025-06-05T07:12:12Z)
• Scaling Laws of Synthetic Data for Language Models [132.67350443447611]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• How to Synthesize Text Data without Model Collapse? [37.219627817995054]
  Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance.<n>We propose token editing on human-produced data to obtain semi-synthetic data.
  arXiv  Detail & Related papers  (2024-12-19T09:43:39Z)
• Little Giants: Synthesizing High-Quality Embedding Data at Scale [71.352883755806]
  We introduce SPEED, a framework that aligns open-source small models to efficiently generate large-scale embedding data. SPEED uses only less than 1/10 of the GPT API calls, outperforming the state-of-the-art embedding model E5_mistral when both are trained solely on their synthetic data.
  arXiv  Detail & Related papers  (2024-10-24T10:47:30Z)
• Towards Effective and Efficient Continual Pre-training of Large Language Models [163.34610964970258]
  Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. This paper presents a technical report for continually pre-training Llama-3 (8B) It significantly enhances the Chinese language ability and scientific reasoning ability of the backbone model.
  arXiv  Detail & Related papers  (2024-07-26T13:55:21Z)
• Online Data Augmentation for Forecasting with Deep Learning [0.33554367023486936]
  This work introduces an online data augmentation framework that generates synthetic samples during the training of neural networks.<n>We maintain a balanced representation between real and synthetic data throughout the training process.<n>Experiments suggest that online data augmentation leads to better forecasting performance compared to offline data augmentation or no augmentation approaches.
  arXiv  Detail & Related papers  (2024-04-25T17:16:13Z)
• Does Synthetic Data Make Large Language Models More Efficient? [0.0]
  This paper explores the nuances of synthetic data generation in NLP. We highlight its advantages, including data augmentation potential and the introduction of structured variety. We demonstrate the impact of template-based synthetic data on the performance of modern transformer models.
  arXiv  Detail & Related papers  (2023-10-11T19:16:09Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.