Pre-training Under Infinite Compute: Rethinking Data Efficiency When Tokens Become Scarce

📰 Medium · Deep Learning

Learn how to rethink data efficiency in pre-training under infinite compute when tokens become scarce, and why it matters for advancing AI capabilities

advanced Published 24 Jun 2026

Action Steps

Analyze the trade-offs between data efficiency and model performance in pre-training under infinite compute
Explore techniques to optimize data efficiency, such as data augmentation and transfer learning
Evaluate the impact of token scarcity on pre-training and develop strategies to mitigate it
Investigate the use of alternative pre-training objectives and evaluation metrics to improve data efficiency
Apply these strategies to real-world pre-training tasks and evaluate their effectiveness

Who Needs to Know This

This article is relevant for AI researchers, machine learning engineers, and data scientists who work on pre-training models and want to optimize their data efficiency. It can help them understand the challenges and opportunities of pre-training under infinite compute and develop strategies to improve data efficiency

Key Insight

💡 Pre-training under infinite compute requires rethinking data efficiency when tokens become scarce, and optimizing data efficiency is crucial for advancing AI capabilities

Key Takeaways

Learn how to rethink data efficiency in pre-training under infinite compute when tokens become scarce, and why it matters for advancing AI capabilities

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Title: Pre-training Under Infinite Compute: Rethinking Data Efficiency When Tokens Become Scarce

URL Source: https://chierhu.medium.com/pre-training-under-infinite-compute-rethinking-data-efficiency-when-tokens-become-scarce-caf6bdd791be?source=rss------deep_learning-5

Published Time: 2026-06-24T23:42:35Z

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# Pre-training Under Infinite Compute: Rethinking Data Efficiency When Tokens Become Scarce

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[Chier Hu](https://chierhu.medium.com/?source=post_page---byline--caf6bdd791be---------------------------------------)

9 min read

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1 hour ago

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## 1. Why I Care About This Problem

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I begin from a simple but increasingly urgent observation: pre-training keeps producing capabilities that were not obvious from first p

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Pre-training Under Infinite Compute: Rethinking Data Efficiency When Tokens Become Scarce

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