<ul data-eligibleForWebStory="true"><li>Enhancements in knowledge distillation techniques have led to improved capabilities in compressing Large Language Models into deployable Small Language Models.</li><li>A new framework called AdvDistill, which is a reward-guided dataset distillation approach, has been proposed to address limitations in traditional distillation methods on reasoning tasks.</li><li>AdvDistill utilizes rewards assigned by rule-based verifiers, based on multiple generations of responses from a teacher model, to train student models effectively.</li><li>The study shows a significant enhancement in student model performance for mathematical and complex reasoning tasks, highlighting the advantages of incorporating reward mechanisms in dataset distillation.</li></ul>

Enhancing Reasoning Capabilities in SLMs with Reward Guided Dataset Distillation

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