Towards Engineering Scaling Laws with Pretraining Data Composition
📰 ArXiv cs.AI
Learn how to engineer scaling laws with pretraining data composition for improved model performance in particle physics and other domains
Action Steps
- Apply power law relationships to model performance in particle physics
- Use high-fidelity simulators to produce synthetic data for pretraining
- Configure pretraining data composition to optimize model scaling
- Test the effects of dataset size and model size on performance
- Analyze the results to inform future model development and optimization
Who Needs to Know This
Researchers and engineers working on large language models and particle physics can benefit from understanding how to engineer scaling laws with pretraining data composition to improve model performance
Key Insight
💡 Pretraining data composition can be engineered to improve model performance in particle physics and other domains
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Key Takeaways
Learn how to engineer scaling laws with pretraining data composition for improved model performance in particle physics and other domains
Full Article
Title: Towards Engineering Scaling Laws with Pretraining Data Composition
Abstract:
arXiv:2606.19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relationships are emerging for large models in particle physics. As with language, empirical studies show that the performance scales as a power law. However, unlike natural language or image domains, fundamental physics has high-fidelity simulators that produce synthetic data
Abstract:
arXiv:2606.19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relationships are emerging for large models in particle physics. As with language, empirical studies show that the performance scales as a power law. However, unlike natural language or image domains, fundamental physics has high-fidelity simulators that produce synthetic data
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