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Are AI Models Distilled from GPT-4 and Claude? The Model Distillation Debate

Cowrie
Cowrie
Dev @ Bswen

The Problem

I was reading through yet another announcement about a Chinese AI model beating GPT-4 on benchmarks. Then I scrolled into the Reddit comments and found something that made me stop:

“The reasoning traces they trained on are poisoned and they are mostly distills” “Because they are all distillations of SOTA frontier models”

These comments weren’t from random trolls - they were from people in the AI community pointing out what they saw as a fundamental problem: many Chinese AI models might not be genuinely trained from scratch. They might be distilled from GPT-4, Claude, and other frontier models.

This led me down a rabbit hole. What exactly is model distillation? Is it cheating or legitimate? And why does it matter?

What is Model Distillation?

Let me explain the technique that started this entire debate.

Model distillation (or knowledge distillation) was invented by Geoffrey Hinton and colleagues in 2015. The original idea was elegant: take a large, powerful model (the “teacher”) and train a smaller model (the “student”) to mimic it.

knowledge-distillation
Traditional Training:
  Human Data → Student Model → Output


Distillation Training:
  Input → Teacher Model (GPT-4) → "Soft" outputs → Student Model
              ↓
         Input → Student Model → Output
              ↓
         Compare: Did student match teacher?

The key insight is that the student doesn’t just learn from the teacher’s final answers - it learns from the teacher’s probability distribution. This is called “soft labels.”

Think of it this way: when GPT-4 answers a question, it’s not just saying “X is correct.” It’s saying “X is 80% likely, Y is 15% likely, Z is 3% likely…” Those probability numbers contain rich information about how the model thinks. A student model can learn from this richer signal than just from correct answers alone.

Why This Matters Now

The original purpose of distillation was practical: put powerful AI on phones and edge devices. A distilled model might be 100x smaller but retain 95% of the capability.

But here’s where things got interesting (and controversial):

  1. Someone realized: What if we distill from frontier models to create competing frontier models?

  2. The ethical question: Is it OK to use GPT-4’s outputs to train a model that will compete with GPT-4?

  3. The detection problem: How would you even know if a model was distilled?

Let me walk you through why this debate has become so heated.

The Evidence That Started It All

When I looked into the Reddit discussions, I found several patterns that raised eyebrows:

1. The Reasoning Trace Problem

One concern is that some models trained on outputs from models like o1 (which show “reasoning traces”) might have learned not genuine reasoning, but the patterns of reasoning - which can be poisoned or systematically biased.

reasoning-traces
GPT-4 Output:
  Let me think about this step by step...
  First, I consider X...
  Then I eliminate Y because...
  Therefore, the answer is Z.


Distilled Model Output:
  Let me think about this step by step...
  First, I consider X...
  Then I eliminate Y because...
  Therefore, the answer is Z.


Similar? Maybe too similar...

2. The Benchmark Paradox

This connects to what I learned about ARC-AGI-2: models that scored 90%+ on some benchmarks scored barely above random on others. Some researchers argue this pattern is consistent with distillation - the model learned to mimic frontier model outputs on certain tasks without genuine understanding.

3. Cost vs. Capability

Here’s something that puzzled me: how can some Chinese models achieve similar performance to GPT-4 at a fraction of the training cost? One explanation is distillation. Another is genuinely efficient architecture. The honest answer is: we don’t know for sure.

The Nuances (Because It’s Not Black and White)

I want to be fair here. The distillation debate has several layers:

Legitimate Uses of Distillation

  • Model compression: Deploying AI on phones, browsers, edge devices
  • Knowledge transfer: Teaching smaller models from larger ones within the same organization
  • Specialization: Creating domain-specific models from general models

Controversial Uses

  • Cross-company distillation: Using another company’s API outputs to train a competing model
  • Undisclosed training: Not telling users that your model was largely trained on outputs from other systems

The Grey Area

Here’s what makes this complicated: ALL language models are trained on text written by humans, including text that was written by AI. The internet is awash with AI-generated content. Where do you draw the line?

training-data-boundary
Web text (some AI-generated) → Model training → This is generally accepted
↓
API outputs from specific model → Model training → This is controversial
↓
Using that model's name in training → Model training → This is explicitly problematic

What This Means for You

If you’re a developer choosing between AI models, here’s what I think about:

For Evaluation

  • Benchmark scores alone aren’t enough - We saw how that played out with ARC-AGI-2
  • Transparency matters - Do you know what a model was trained on?
  • Cost vs. capability - If something seems too cheap, ask why

For Development

  • Know your training data - Understanding sources matters for legal and ethical reasons
  • Consider the ethics - Using another company’s outputs without permission raises concerns
  • Look for independent verification - Third-party evaluations (like ARC-AGI-2) matter more than self-reported benchmarks

For the Industry

This debate points to bigger questions:

  • How do we verify AI capabilities independently?
  • What constitutes “original” training vs. derivative work?
  • Should there be disclosure requirements for training methodologies?

The Bottom Line

When I first heard “Chinese models are distilled from GPT-4,” I assumed it was just another tech controversy. But the more I learned, the more I realized this touches on fundamental questions about AI development:

  • What does it mean for a model to be “intelligent”?
  • How do we measure genuine capability vs. sophisticated mimicry?
  • What’s the difference between learning from the internet and learning from an API?

I don’t have definitive answers. But I do know this: the next time you see a model announcement claiming to beat GPT-4, it’s worth asking not just “how well does it perform?” but also “how was it trained - and from what?”

The distillation debate isn’t just about specific models. It’s about how we evaluate progress in AI at all.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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