# Provable Benefits of In-Tool Learning for Large Language Models

## TL;DR

This paper demonstrates that tool-augmented language models with external retrieval can recall facts unboundedly, outperforming models that rely solely on memorization within their weights, supported by theory and experiments.

## Contribution

It provides the first theoretical proof that tool-use enables unbounded factual recall, surpassing memorization limits, and empirically validates the advantages of in-tool learning.

## Key findings

- Tool-use models outperform memorizing models in factual recall.
- Memorization capacity is limited by model parameters.
- Teaching tool-use is more effective than finetuning facts into memory.

## Abstract

Tool-augmented language models, equipped with retrieval, memory, or external APIs, are reshaping AI, yet their theoretical advantages remain underexplored. In this paper, we address this question by demonstrating the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall. We show that the number of facts a model can memorize solely in its weights is fundamentally limited by its parameter count. In contrast, we prove that tool-use enables unbounded factual recall via a simple and efficient circuit construction. These results are validated in controlled experiments, where tool-using models consistently outperform memorizing ones. We further show that for pretrained large language models, teaching tool-use and general rules is more effective than finetuning facts into memory. Our work provides both a theoretical and empirical foundation, establishing why tool-augmented workflows are not just practical, but provably more scalable.