MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings

• URL: http://arxiv.org/abs/2503.03008v2
• Date: Mon, 19 May 2025 13:39:47 GMT
• Title: MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings
• Authors: Andrea Gurioli, Federico Pennino, João Monteiro, Maurizio Gabbrielli,
• Abstract summary: Self-Distillation is a principled approach to trading inference cost for accuracy across various code understanding tasks.<n>Our architecture improves text-to-code and code-to-code search by targeting specific encoder layers as exit heads.<n>We release a new dataset created through code translation that extends text-to-code benchmarks with cross-language code-to-code pairs.
• Score: 2.1262605464247812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying language models often requires navigating accuracy vs. performance trade-offs to meet latency constraints while preserving utility. Traditional model distillation reduces size but incurs substantial costs through training separate models. We introduce ModularStarEncoder (MoSE), a 1-billion-parameter multi-exit encoder for code retrieval and classification that employs a novel Self-Distillation mechanism. This approach significantly enhances lower-layer representations, enabling flexible deployment of different model portions with favorable performance trade-offs. Our architecture improves text-to-code and code-to-code search by targeting specific encoder layers as exit heads, where higher layers guide earlier ones during training-improving intermediate representations at minimal additional cost. We further enhance MoSE with a repository-level contextual loss that maximizes training context window utilization. Additionally, we release a new dataset created through code translation that extends text-to-code benchmarks with cross-language code-to-code pairs. Evaluations demonstrate the effectiveness of Self-Distillation as a principled approach to trading inference cost for accuracy across various code understanding tasks.

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