Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study

• URL: http://arxiv.org/abs/2609.03702v1
• Date: Thu, 03 Sep 2026 11:41:09 GMT
• Title: Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study
• Abstract summary: General-purpose code embeddings power tools for code search, classification, and retrieval.<n>We study an alternative: contrastive pretraining of small encoders with synthetically generated natural-language descriptions emphasizing code functionality and intent.
• Score: 8.409799680956013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: General-purpose code embeddings power tools for code search, classification, and retrieval. Compact transformer encoders for code typically rely on either human-written docstrings (labor-intensive and inconsistent) or mined structural signals such as execution traces (setting-specific and costly to collect). We empirically study an alternative: contrastive pretraining of small encoders with synthetically generated natural-language descriptions emphasizing code functionality and intent, paired with code in a dual-encoder framework at training and discarded at inference. We benchmark this approach against pretraining-based baselines, generalist LLMs, and embedding-specific models on eight retrieval, classification, and generation tasks across C, C++, and Java. Synthetic semantic supervision yields statistically significant gains over pretraining baselines of the same inference-time size on five of eight tasks, with parity on two more; once fine-tuned, it matches or exceeds zero-shot models two orders of magnitude larger on classification, and it stays on par with execution-aware supervision at matched pretraining data, suggesting a scalable, effective alternative to existing code-representation paradigms.

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