Towards Engineering Scaling Laws with Pretraining Data Composition

2026-06-18 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A study on neural scaling laws in particle physics, published on 2026-06-18, demonstrates how pretraining data composition can engineer model performance. While scaling laws are well-known for large language models, their application in particle physics, particularly for classifying hadronic jets from high-energy particle collisions, is emerging. Unlike natural language or image domains, particle physics benefits from high-fidelity simulators that generate synthetic data cheaply. This unique advantage allows for scaling regimes where additional data is more cost-effective than increasing model parameters. The research specifically shows that by incorporating pretraining data that is more diverse and better aligned with the downstream classification task, the scaling behavior can be intentionally shifted towards requiring more data rather than larger models to achieve performance improvements.

Key takeaway

For Machine Learning Engineers developing models in scientific domains with access to high-fidelity simulators, you should prioritize strategic pretraining data composition. By actively engineering your datasets to be more diverse and closely aligned with specific downstream tasks, you can shift model scaling behavior. This approach allows you to achieve performance gains by investing in more data rather than solely relying on larger, more computationally expensive models, optimizing resource allocation.

Key insights

Pretraining data composition can engineer neural scaling laws in particle physics, favoring data-rich regimes over larger models.

Principles

• High-fidelity simulators enable data-rich scaling in physics.
• Diverse, task-aligned data shifts scaling towards data needs.

Method

Engineering pretraining data composition by including more diverse and task-aligned synthetic data to influence neural scaling behavior.

In practice

• Generate synthetic data to optimize scaling laws.
• Prioritize data diversity for jet classification models.
• Align pretraining data closely with downstream tasks.

Topics

• Neural Scaling Laws
• Particle Physics
• Hadronic Jet Classification
• Synthetic Data
• Pretraining Data Composition
• Machine Learning Engineering

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.