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arXiv · 2503.16870

Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs

Anshumann·Mohd Abbas Zaidi·Akhil Kedia·Jinwoo Ahn·Taehwak Kwon·Kangwook Lee·Haejun Lee·Joohyung Lee
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Abstract

Knowledge distillation can be a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. However, successfully applying this to pre-training remains largely unexplored. In this work, we prove that naive approaches for sparse knowledge distillation such as caching Top-K probabilities, while intuitive, provide biased estimates of teacher probability distribution to the student, resulting in suboptimal performance and calibration. We propose an importance-sampling-based method `Random Sampling Knowledge Distillation', which provides unbiased estimates, preserves the gradient in expectation, and requires storing significantly sparser logits. Our method enables faster training of student models with marginal overhead (<10%) compared to cross-entropy based training, while maintaining competitive performance compared to full distillation, across a range of model sizes from 300M to 3B.

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Anshumann, Mohd Abbas Zaidi, Akhil Kedia, Jinwoo Ahn, Taehwak Kwon, Kangwook Lee, Haejun Lee, Joohyung Lee. 2025-03-21. Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs. https://arxiv.org/abs/2503.16870

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