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arXiv · 2605.15649

Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search

Pranav Somu·Advay Balakrishnan·Stepan Kravtsov·Aaron McDaniel·Jason Zutty
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Abstract

Developing effective surrogates (performance predictors) for Neural Architecture Search (NAS) typically requires expensive fine-tuning or the engineering of complex representations. We propose a low-cost embedding strategy that leverages the inductive bias of Language Models (LMs) to eliminate these overheads. By representing architectures as PyTorch class definition text, we demonstrate that off-the-shelf LMs act as competitive feature extractors without NAS-specialized fine-tuning. The final predictor is constructed by passing the extracted Code-Oriented LM Embeddings (COLE) through a lightweight regression head. We also investigate strategies to improve embedding quality and utilization. Our experiments on the NAS-Bench-201 and einspace search spaces reveal that raw code inputs yield higher predictive performance than other text-based encodings (e.g., ONNX-to-text encodings) when using frozen LMs. We also observe COLE drives superior surrogate-assisted search using the BANANAS algorithm in NAS-Bench-201. When optimizing for CIFAR-100 performance, replacing structural path encodings with COLE for architecture representation allows for a 34% decrease in the evaluation budget required to reach within 1% of the fittest architecture in the search space (by test accuracy). As any neural architecture can be represented as code, these findings establish COLE as a versatile and efficient foundation for advancing NAS.

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Pranav Somu, Advay Balakrishnan, Stepan Kravtsov, Aaron McDaniel, Jason Zutty. 2026-05-15. Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search. https://doi.org/10.1145/3795101.3805435

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