Knowledge Distillation Is Not a Shortcut; It’s How AI Gets Smaller and Smarter

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Knowledge Distillation: The Truth Behind the Large Model's "Energy Absorption Technique," Is DeepSeek Really Cutting Corners?

In 2025, a major event shook the AI world—DeepSeek built a model capable of rivaling OpenAI with a training cost of only about $6 million. This should have been a victory for technology democratization, but OpenAI quickly accused DeepSeek of using distillation technology, calling it a "shortcut" and even describing it as an "Energy Absorption Technique" from the East.

Overnight, the term "distillation" was thrust into the spotlight. It sounds like a mysterious chemical operation and a forbidden martial arts technique. What is the truth? Is distillation a sneak attack, or is it an open secret of "power transfer" in the AI world? Today, let's break it down completely with a light-hearted explainer.

1. What is Distillation? From the Chemistry Flask to the AI Model

Distillation is originally a chemical term: heating a mixed liquid to vaporize a component, then cooling and condensing it to extract a purer essence. For example, distilling high-concentration alcohol from a fermented liquid is about "discarding the dross and extracting the essence."

The core idea of knowledge distillation in large models is exactly the same. The only difference is that the subject has changed from liquid to massive amounts of knowledge.

In knowledge distillation, we have two roles:

Teacher Model: An already trained, super-powerful large model, like GPT-4, Claude, or the object that DeepSeek-R1 is trying to "learn" from.
Student Model: A smaller model with fewer parameters and a lighter structure, whose goal is to acquire abilities close to the teacher's at a lower cost.

The entire process is—distilling the "knowledge essence" from the teacher model's brain and transferring it to the student model. This is very similar to the scene in "Demi-Gods and Semi-Devils" where Wu Yazi transfers his lifelong internal energy directly to Xuzhu. Xuzhu doesn't need to train hard for decades; he instantly gains a profound foundation.

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2. How Does the Small Model Learn? Memorizing Answers, or Stealing Thought Processes?

Imagine a small model apprenticing itself to a large model. What is the simplest way to learn? It's to constantly ask the large model questions and then memorize the standard answers. This process can be summarized as:

Question: What is this picture? Large Model Answer: Cat.

The small model then rigidly remembers: this picture = cat. This method of "only remembering the final answer" is called hard labels in knowledge distillation.

Hard labels are like giving you an exercise book with only questions and final answers; you pass the exam purely by memorizing the answers. It can indeed handle some situations, but as soon as the question is slightly modified, the model will be confused—because it hasn't learned the underlying patterns, only that "the answer to this question is cat."

So what does truly effective learning look like? The answer is: imitating the large model's "way of thinking."

When a large model answers a question, it doesn't just spit out the word "cat." Internally, it actually calculates a set of probability distributions: Cat 80%, Dog 10%, Tiger 8%, Chick 2%... This set of probabilities is the large model's true understanding of the question. We call these soft labels.

Soft labels contain extremely rich information:

Cats and dogs have similarities (both are common pets), so the dog also gets a 10% probability.
Cats and tigers are more alike (both are felines), but the similarity isn't as high as with dogs, so the tiger gets 8%.
A chick is very different, with a probability of only 2%.

This sense of analogy and association is an intuition precipitated by the large model after consuming massive amounts of data, which we call dark knowledge. It cannot be summarized simply by the sentence "the answer is cat."

Therefore, the essence of distillation is: letting the small model approximate the teacher model's entire probability distribution, rather than memorizing that single standard answer. It needs to learn "why cat is the most likely, and to what degree dog and tiger are similar," the entire contour of the teacher's thinking about the problem.

3. A Kitchen Analogy to Make You Completely Understand

If the technical description above is still a bit confusing, let's use a vivid example—training a chef.

Option 1: Giving a Recipe (Hard Labels) You throw a newcomer a recipe book with precise instructions: 15g sugar, 3g salt, oil temperature 180 degrees. The newcomer follows it, and the resulting dish is merely "edible." Once the stove is changed or slightly different ingredients are bought, they are completely lost. They can only replicate, not adapt.

Option 2: Letting a Michelin Chef Demonstrate Personally (Soft Label Distillation) The newcomer follows the chef, observing how the chef tastes a spoonful of broth and knows "it needs a bit more umami"; how they judge the heat from subtle changes in the oil smoke; how they soften butter with the warmth of their palm... This knowledge has no precise formula, but it is a complete set of probabilistic intuition for perception and decision-making. Immersed in this environment day after day, the newcomer eventually internalizes the chef's way of thinking and can handle an unfamiliar kitchen with ease.

Knowledge distillation aims for this second effect. It's not about compressing knowledge into rigid rules, but about "growing" a mode of judging the world within the small model.

4. Back to the Controversy: Did DeepSeek Really Break the Rules by Using Distillation?

Once we understand the nature of distillation, our perspective on OpenAI's accusations becomes much more nuanced.

First, knowledge distillation is not some secret "dark art." It was formally proposed by deep learning pioneer Hinton and others in 2015 and has been a standard tool in the model compression toolbox. The industry widely uses it to transfer the capabilities of large models to lightweight models on mobile phones and in vehicles, allowing AI to run locally without the cloud. Distillation is a key bridge for AI deployment.

Second, distillation itself does not violate any technical ethics. The real point of contention lies in the "terms of use." Many large model service agreements prohibit users from using their outputs to train competing models. If DeepSeek did indeed call OpenAI's API on a large scale and used the returned probability distributions (or repeatedly queried to recover dark knowledge) to train its own model, and this behavior violated OpenAI's terms of service at the time, then this is a commercial and legal issue, not a crime of technical principle.

Third, the "Energy Absorption Technique" rumor from the outside world is actually biased. True distillation requires the student model to have sufficient capacity and a learning framework; it's not mindless absorption. Moreover, DeepSeek's papers and public information show it heavily relies on reinforcement learning, architectural innovation, and extreme engineering optimization. The core of the $6 million cost comes from meticulous calculation across the entire training chain, far from being summarized by a phrase like "data theft."

In any case, this controversy has pushed an important technical concept into the public eye: Distillation is a legitimate way for AI to inherit wisdom. And how to define "legitimate use" will be a question the entire industry must answer together in the future.

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5. The Future of Distillation: Making Power Lighter

Controversy aside, the core value of knowledge distillation remains unchanged: it prevents the wisdom of top-tier AI from being trapped inside expensive supercomputers.

In the future, the real-time translation, smart photo album classification, and local voice assistants on your phone will likely have inherited a sophisticated world-understanding model from some cloud giant through distillation. They run fast, consume little power, and protect privacy. Just as after Wu Yazi transferred his power to Xuzhu, Xuzhu could roam the world freely without carrying the heavy mountain of his predecessor.

Distillation is precisely this kind of technology that makes AI lightweight, universal, and powerful. Understanding it gives you a sharper pair of eyes when looking at the endless stream of AI news.

This article is a creative extension of study notes, striving to deconstruct hardcore knowledge in plain language. If it was helpful, likes and saves are welcome, and feel free to discuss your views on the DeepSeek incident in the comments.