Let's Learn About Knowledge Distillation!

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knowledge-distillation frontier-models lawsuits ai-ethics legal

Summary

The article argues that frontier model providers who criticize knowledge distillation are hypocritical, as their own legal defense against copyright lawsuits relies on the same principle of not directly storing or touching data.

Knowledge Distillation is exceedingly easy to do and has been around since the inception of large models. Since it cannot be performed by a 5th grader, it remains a complete black box to most. All of a sudden, people with money do not like Knowledge Distillation. So, in order to look like they are smarter than a 5th grader, everyone all of a sudden is talking about Knowledge Distillation. The people with money who build the models also do not like getting sued. They have utilized one singular argument since the inception of this in every lawsuit, they are not actually touching or storing the data directly itself. I agree with every frontier model provider that has ever made this argument. They are correct. It is exactly why they win their lawsuits. Knowledge Distillation falls into literally the same category. Every single argument that the frontier model providers utilize, have utilized, and will continue to utilize in defense of this, is also applicable to Knowledge Distillation. You cannot just carve it out. Cake for me but not for thee? So, what exactly is it that people are asking for when they make these arguments? Do you like getting sued? Because making these arguments as a frontier model provider, is how you lose lawsuits. It is the most short sighted argument you could ever make. I Can't Read I Only Like Video
Original Article

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KDFP: A first-principles approach to knowledge distillation in large language models

arXiv cs.LG

KDFP: A first-principles approach to knowledge distillation in large language models Source: https://arxiv.org/html/2610.10854 Ryan Swift & Konstantinos PsounisAffiliation:Thomas Lord Department of Computer ScienceAffiliation:University of Southern CaliforniaAffiliation:Los Angeles, CA 90007, USAEmail:[{ryanswif,kpsounis}@usc.edu](mailto:) ###### 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.