AI Distillation: A Key to Cheaper Models or a Recipe for More AI Delusions?

As tech giants like Meta, OpenAI, and Microsoft compete to build more intelligent, affordable and cost-effective AI, they are intensively adopting “distillation” — a method that is believed to reduce the costs and computational power needed to run AI models.But while this technique is gaining momentum as a “golden ticket” to cheaper AI, there is a catch: Is distillation truly the solution, or could it lead to more unreliable, error-prone models that suffer from hallucinations?To answer this, we need to explore what distillation really stands for, weigh the pros and cons, and figure out how distillation and hallucinations are connected. Let’s get into it.

A New Breakthrough or An Old Trick?At its core, AI distillation stands for the process of training a smaller, “weaker” AI model utilising synthetic data generated by a more powerful “strong” model. They are often called “student” and “teacher,” respectively.Putting it simply, it is like teaching a beginner in any game by showing them a series of practical lessons instead of making them learn the rules from scratch.



In this case, the weaker model can learn key patterns and make decisions using way less computational power.But is this truly a groundbreaking approach, or just an old idea given a new name?While the term “AI Distillation” might be recent, the underlying concept is not fresh at all. The idea of using simpler models to approximate complex systems has been around for quite some time, often under various names, like “knowledge transfer” or “teacher-student learning.

” For instance, this research, dated back to 2018, breaks down the entire concept — which confirms that it is not just a modern trend.What makes it feel new is the way it has been applied in the context of today's resource-hungry models. Back in the day, it might have been used in smaller-scale machine learning (ML) applications, but as AI models grow, distillation has gained broader implementation.

Overall, it is indeed a clever tool, but not a breakthrough at its core. It is simply a refined approach to an old trick, one that is becoming increasingly popular in today’s AI development scene.AI’s Mentor Model: Perks and PitfallsNow, despite AI distillation being a more clever approach to an old strategy, it is not without trade-offs.

The big question here: What do we gain and lose by using a smaller model to mimic a larger one? Let’s take a look at the pros and cons of this method.One of the most obvious advantages is efficiency. Distilled models are significantly lighter, which means that they can literally operate on mobile devices.

And the point is that it is almost impossible with large-scale models. Is this only in theory? Not at all. Optimised versions of Meta’s LlaMA family, like TinyLLaMA, are already being deployed into lightweight AI apps that run on phones without cloud access.

The result? Faster response times and reduced costs for both companies and regular users.Another strong point — data security. Distillation makes it possible to create smaller models that can run locally without relying on the cloud.

That is a game-changer in industries like finance, where data privacy is critical and cloud-powered solutions could pose risks. In these cases, local deployment is not just an option — it is a necessity if you want to keep sensitive data safe.These advantages, however, do not come for free.

While distillation works well for tasks like data analysis, it could cause a loss of nuance. The “weaker” model often struggles with emotional intelligence and “subtlety.” Just imagine a customer service AI that answers questions directly and efficiently but falls short in picking up the tone or responding empathetically — absolutely not warm or humanlike.

It could easily turn many people away, given a widespread distrust of AI and the discomfort some feel when talking to a chatbot instead of a real person.At the same time, the risk of hallucination is also there. When the model is distilled, it doesn’t just learn the good stuff — it may just as easily pick up its “teacher’s” bad habits.

In fact, it could even make worse mistakes by trying to simplify things too much. A likely result is that it may deliver bizarre or even completely incorrect information.Which brings us to the next part of this discussion.

AI Makes Stuff Up — Could Distillation Handle It?In a nutshell, “hallucination” refers to when AI, which seems quite smart, delivers false or non-relevant information. And as I already mentioned, when AI is distilled, the risk of this happening becomes much more probable. But is everything really so bad?Although the “student” model could misinterpret the “teacher’s” information — literally copying the answers without understanding the work — there is an interesting twist: distillation, in the right hands, can actually help.

If users carefully select the right responses from a bigger model — basically, feed the “student” only the best examples — they may notice that the smaller model makes fewer errors. It is as simple as ordinary teaching. If the teacher is thoughtful and the lessons are well-designed, the student might avoid the mistakes.

Moreover, some researchers are even utilising distillation to clean up training data and make models more reliable. In 2023, researchers at Google introduced a method of “Distilling Step-by-Step,” where they integrated the intermediate reasoning steps into training data. Due to this, distilled models have learned how to arrive at correct answers more efficiently.

So, does AI distillation, in fact, help combat hallucinations? It depends. But if done right, it certainly can help build models that are not only smarter and faster but also more factually precise.The Bottom LineAI distillation is getting popular for a reason: it offers a smarter, faster, and more cost-effective way to deploy AI in resource-limited environments.

The main takeaway is that while distillation does carry some risks — particularly with hallucination — it can also help tackle these risks when approached carefully.This can even be confirmed by the example of the biggest market players. Remember how the DeepSeek neural network made headlines in the media not so long ago? Its R1 model uses distillation to create a smaller, more efficient AI that still performs well.

They trained it on data from larger models like OpenAI’s ChatGPT, which allowed them to build a competitive AI system at a much lower cost.Eventually, AI distillation is neither a magic wand nor a fatal flaw. It’s a tool — and like any tool, its effectiveness depends solely on how prudently you use it.

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