Let's Learn About Knowledge Distillation!

Reddit r/ArtificialInteligence News

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

Similar Articles

Hybrid Policy Distillation for LLMs

arXiv cs.CL

Introduces Hybrid Policy Distillation (HPD), a unified knowledge distillation approach that balances forward and reverse KL divergences and combines off-policy data with lightweight on-policy sampling, improving LLM compression across math, dialogue, and code tasks.

The Distillation Game: Adaptive Attacks & Efficient Defenses

Hugging Face Daily Papers

This paper studies distillation attacks where model outputs can enable imitation, proposing a minimax game framework and a forward-pass-only defense called Product-of-Experts, showing that adaptive students recover more capability than passive evaluation suggests.