Pretraining on the Test Set Is All You Need

Rylan Schaeffer

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arXiv · 2309.08632

Pretraining on the Test Set Is All You Need

Rylan Schaeffer

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Abstract

Inspired by recent work demonstrating the promise of smaller Transformer-based language models pretrained on carefully curated data, we supercharge such approaches by investing heavily in curating a novel, high quality, non-synthetic data mixture based solely on evaluation benchmarks. Using our novel dataset mixture consisting of less than 100 thousand tokens, we pretrain a 1 million parameter transformer-based LLM \textbf{phi-CTNL} (pronounced ``fictional") that achieves perfect results across diverse academic benchmarks, strictly outperforming all known foundation models. \textbf{phi-CTNL} also beats power-law scaling and exhibits a never-before-seen grokking-like ability to accurately predict downstream evaluation benchmarks' canaries.

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Rylan Schaeffer. 2023-09-13. Pretraining on the Test Set Is All You Need. https://arxiv.org/abs/2309.08632

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