<ul><li>The paper introduces Adversarial Knowledge Distillation (AKD) as a novel approach to enhance Large Language Models (LLMs) for code generation tasks.</li><li>AKD leverages adversarially generated synthetic datasets to distill the capabilities of larger models into smaller, more efficient ones.</li><li>The goal of AKD is to improve the robustness, reliability, and security of Code-LLMs while enhancing parameter-efficiency by stress-testing and refining their reasoning capabilities.</li><li>This approach aims to address concerns about the quality, safety, and reliability of code generated by Code-LLMs, particularly in the face of scaling challenges and limited high-quality training data.</li></ul>

AKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks

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