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

TL;DR

BeyondWeb introduces a high-quality synthetic data generation framework that significantly improves pretraining efficiency and performance for large language models, outperforming existing datasets and providing insights into synthetic data optimization.

Contribution

The paper presents BeyondWeb, a novel synthetic data generation framework that enhances pretraining datasets, outperforming state-of-the-art datasets and offering insights into factors affecting synthetic data quality.

Findings

BeyondWeb outperforms Cosmopedia and Nemotron-Synth in benchmark evaluations.

Training on BeyondWeb data yields faster training and better model performance.

Insights into data quality factors and their impact on pretraining effectiveness.

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.