Easy to Understand! A Simple Explanation of DeepSeek's Distillation Technology
DeepSeek’s distillation technology simplifies complex models, making them more accessible without losing key information. While most AI models struggle with distillation, DeepSeek’s approach stands out.
Many people don’t really understand what the distillation technology used by DeepSeek is all about. Let me explain with an example.
Imagine the teacher in class gives a super difficult problem. Except for one top student, everyone else is unable to solve it because their brains just aren’t equipped to handle it.
After class, the top student thinks more about the problem, simplifies some parameters and steps based on the teacher's approach, making it easier for the rest of the class to understand. This helped most of the class figure it out.
What the top student did here is called distillation.
Distillation isn’t always successful because simplifying things means losing some information. If the lost information is important, the whole system can break down. In fact, nearly every large AI model in the world is trying to do distillation, but the results usually aren't great. DeepSeek, however, might be the first one to do it successfully while keeping the model close to the original.
So, the key to distillation is deciding what can be simplified and what needs to be kept. DeepSeek is open-source and has published several papers explaining how it works. That's why people all over the world are trying to replicate its algorithms, and several companies have already succeeded.
By the way, in most advanced fields, especially in computer science, there’s very little cheating or plagiarism in research results. This only applies to high-level research, though—low-level stuff that no one cares about doesn’t count.
The reason is simple: journals or conferences always require you to provide your source code. If the results are significant, people from all around the world will try to replicate them.
If the same source code or algorithm is run on any computer in the world, the results should always be the same. If someone’s result doesn’t match, it could be a problem with their setup. If everyone gets a different result, then there’s definitely an issue with yours.
In other fields, like biology, chemistry, or social sciences, it’s much harder to replicate results because every sample is unique. No one can really say what exactly in a sample caused the final outcome. So, even if no one can replicate your results, it doesn’t necessarily mean something is wrong.
We can have a rational discussion about how innovative DeepSeek really is, but when it comes to whether it’s original or innovative, there’s no real debate.
It definitely is.