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arXiv · 2508.17400

Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs

Jacob Portes·Connor Jennings·Erica Ji Yuen·Sasha Doubov·Michael Carbin
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Abstract

How does retrieval performance scale with pretraining FLOPs? We benchmark retrieval performance across LLM model sizes from 125 million parameters to 7 billion parameters pretrained on datasets ranging from 1 billion tokens to more than 2 trillion tokens. We find that retrieval performance on zero-shot BEIR tasks predictably scales with LLM size, training duration, and estimated FLOPs. We also show that In-Context Learning scores are strongly correlated with retrieval scores across retrieval tasks. Finally, we highlight the implications this has for the development of LLM-based retrievers.

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Jacob Portes, Connor Jennings, Erica Ji Yuen, Sasha Doubov, Michael Carbin. 2025-08-24. Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs. https://arxiv.org/abs/2508.17400

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