aiqbench analysis
KDFP addresses a critical gap in LLM optimization by shifting focus from post-training skills to general knowledge distillation, enabling more efficient deployment on edge devices. The methodology achieves significant gains in both accuracy and training speed through a novel white-box approach that reduces ephemeral parameters. This work advances the practical viability of running capable models in resource-constrained environments without sacrificing performance.
KDFP: A first-principles approach to knowledge distillation in large language models
arXiv:2610.10854v1 Announce Type: new Abstract: Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the representations of larger, more capable teacher models. Much of the recent work in the distillation of large language models (LLMs) has focused on distilling abilities learned during post-training, such as instruction following, chain-of-thought reasoning, and tool usage. This has left a large research gap in general knowledge distillation for LLMs, which is essential for developing efficient and private systems suitable for deployment on edge devices. We take a first-principles approach, evaluating previous lessons from prior works and conducting new explorations to develop a distillation methodology suitable for modern LLMs. We present KDFP, a novel methodology for white-box general knowledge distillation in LLMs. We demonstrate that KDFP outperforms existing methods by 1.6% $-$ 4.9% across 9 benchmarks while increasing training efficiency by up to 99.1% through ephemeral parameter reduction.
Abstract:Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the representations of larger, more capable teacher models. Much of the recent work in the distillation of large language models (LLMs) has focused on distilling abilities learned during post-training, such as instruction following, chain-of-thought reasoning, and tool usage. This has left a large research gap in general knowledge distillation for LLMs, which is essential for developing efficient and private systems suitable for deployment on edge devices. We take a first-principles approach, evaluating previous lessons from prior works and conducting new explorations to develop a distillation methodology suitable for modern LLMs. We present KDFP, a novel methodology for white-box general knowledge distillation in LLMs. We demonstrate that KDFP outperforms existing methods by 1.6% $-$ 4.9% across 9 benchmarks while increasing training efficiency by up to 99.1% through ephemeral parameter reduction.
Machine Learning (cs.LG)
arXiv:2610.10854 [cs.LG]
(or arXiv:2610.10854v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.10854 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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