Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation
Reverse Kullback-Leibler (RKL) divergence has recently emerged as the preferred objective for large language model (LLM) distillation, consistently outperforming forward KL (FKL), particularly in regimes with large vocabularies and significant teacher-student capacity mismatch, where RKL focuses learning on dominant modes rather than enforcing dense alignment. However, RKL introduces a structural limitation that drives the student toward overconfident predictions. We first provide an analysis of
Record details
Published: 31 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Children & education
Retrieved: 14 July 2026
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ethics.ai (31 March 2026), “Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation,” evidence record 6523, https://ethics.ai/record/6523 (originally published by arXiv).
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