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

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Researchers found that training small code-encoder models using synthetic, AI-generated natural language descriptions outperforms traditional methods using human-written docstrings or complex execution traces. By using high-quality synthetic intent descriptions during training and discarding them at inference, small models achieve massive performance gains in code search and classification. This proves that synthetic data pipelines are increasingly the most efficient way to 'distill' logic into compact, edge-ready models.