Here's why AI model distillation has gained spotlight in AI race between US and China, explained

AI model distillation, a technique that allows developers to shrink powerful artificial intelligence models into cheaper, more efficient systems, has now become the latest battleground between the United States and China in the race for AI dominance, as per a Reuters report.

Explaining the process, it noted that at its core, model distillation uses output generated from more powerful AI systems to train a smaller platform with relatively limited capabilities and computing resources.

It is not a new technique and has been a standard tool when it comes to AI research but has become the bone of contention over who holds the rights to advanced AI capabilities and whether these aspects can be transferred without consent of the companies involved.

The US government and leading AI companies have accused Chinese rivals of using the technique to extract capabilities from proprietary models, opening a new front in an increasingly bitter competition over technological leadership. Here's a look at some of the key highlights about the practice at the centre of the debate:

What is model distillation? Explained

The largest AI models, known as frontier models, require enormous amounts of computing power, data and investment to train.

Model distillation offers a way to create smaller systems by using a large "teacher" model to train a smaller "student" model.

The teacher generates examples, such as answers and computer code, which are then used as training material for the student.

The smaller model is not a replica of the teacher. It does not inherit the teacher's weights, architecture or full capabilities. Instead, it learns selected behaviours that enable it to perform specific tasks more efficiently.

Why does distillation matter?

The appeal of distillation is that it can make AI cheaper and easier to deploy.

A frontier model may require large data centres and expensive chips to operate. Distilled models can run on less powerful hardware and be tailored for specific tasks.

That makes them appealing to companies and governments looking to deploy AI more widely, from devices and factories to vehicles and private networks.

Why are reasoning traces important?

Recent AI systems have increased interest in transferring not only final answers but also the steps used to reach them.

These "reasoning traces" can show a smaller model how to approach a difficult problem rather than simply what answer to produce.

Florian Tramèr, an assistant professor at ETH Zurich who researches machine-learning security, compared the process to human learning.

"If I give you a book of complicated math problems with final solutions, you will have a much harder time learning how to solve problems than if I gave you detailed solutions that describe all steps to take," he said.

As reasoning traces have become more valuable, access to AI outputs has become more sensitive because they may expose some of the methods advanced systems use to tackle complex problems.

Who uses distillation?

Distillation is a widely used AI training technique, not an inherently improper practice.

U.S. researchers and companies have long used it, including Stanford University's Alpaca project and Microsoft's Orca research, which relied on outputs from more advanced models to improve smaller ones.

Chinese researchers have also used outputs from U.S. models in public research projects, including efforts to create Chinese-language instruction models.

The key difference lies in access. Open-weight models let researchers inspect and modify underlying parameters. Closed models, such as OpenAI's ChatGPT and Anthropic's Claude, remain under company control and are typically accessed through proprietary interfaces or APIs.

Why has distillation become a US-China issue?

The controversy is less over distillation itself and more about unauthorised extraction.

AI companies argue there is a distinction between legitimate research and systematically harvesting outputs from proprietary models to replicate commercially valuable capabilities.

Anthropic has accused Chinese entities including DeepSeek, Moonshot and MiniMax of conducting large-scale campaigns to obtain capabilities from Claude models.

The company said those efforts targeted capabilities including software engineering and advanced reasoning.

OpenAI has also said it has detected attempts by Chinese actors to use its models for distillation-related purposes.

No Chinese companies have accused U.S. rivals of distilling closed-source models so far.

(With inputs from Reuters)