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arXiv · 2501.18916

LLM Program Optimization via Retrieval Augmented Search

Sagnik Anupam·Alexander Shypula·Osbert Bastani
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Abstract

Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find that performing contextual retrieval based on an LLM-generated natural language description significantly outperforms retrieval based on the source code. We also propose AEGIS, a method for improving interpretability by decomposing training examples into ''atomic edits'' that are significantly more incremental in nature. We show that RAS performs up to 2.06$\times$ better than prior state-of-the-art blackbox adaptation strategies on optimizing C++ programs, and that AEGIS performs up to 1.37$\times$ better while making significantly smaller edits. We also show that using RAS improves the mean runtime percentile of Python programs by 10.27 compared to baselines.

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Sagnik Anupam, Alexander Shypula, Osbert Bastani. 2026-06-23. LLM Program Optimization via Retrieval Augmented Search. https://arxiv.org/abs/2501.18916

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