Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search
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Computer Science > Machine Learning
arXiv:2605.15649 (cs)
[Submitted on 15 May 2026]
Title:Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search
View a PDF of the paper titled Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search, by Pranav Somu and 4 other authors
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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.
| Comments: | This is an extended version of work accepted to GECCO 2026. Our code is available at this https URL |
| Subjects: | Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2605.15649 [cs.LG] |
| (or arXiv:2605.15649v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15649
arXiv-issued DOI via DataCite (pending registration)
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| Related DOI: | https://doi.org/10.1145/3795101.3805435
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