Model Distillation?

sufal kc

5 min read

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Mar 9, 2026

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…but what? What really a model Distillation means? And what they are refering by saying that?

How is Model actually developed??

There are generally 3 stages to build a AI Models:

Analogy:

Planning → Ingredients → Chef → Practice → Taste Test → Open Restaurant

Actual Steps:

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1. Pre Training

In this step it collects data, filter the usable one, appropriate model is selected and model is trained.

Simply it “Read the internet, Predict the next word and Learn everything”

• Model reads trillions of tokens of text: Books, Wikipedia, code, forums, academic paper etc,
• Learn grammar, facts, reasoning, code: From the data provided,
• Objective -> predict the next token: what comes next?
• Produce a “base model”: Able to generate next text, but can’t hold conversation yet.

2. Fine Tuning

Teaching the base model to follow the instructions.

• Quality over quantity, so much smaller than pretraining,
• can also be domain specific such as medical, legal to understand jargon and context easily,
• Trained on curated pairs [instruction, response].

For model generating text token by predicting the probability of the next token based on the context.

Before Tuning

• The model has not learned factual Q&A well,
• It tends to continue question pattern rather than provide an answer.

After Tuning

• It reshapes the probability distribution making correct tokens,
• The models now recognize the Q&A pattern and answers questions helpfully.

3. RLHF

Reinforcement Learning from Human Feedback: teaching model what humans actually want as output.

• Generated multiple response are ranked from best to worse based on feedback,
• Optimized model to maximize the reward scores and learn from human preference.

This task is really expensive. Reports about the GPT single model cost are:

Pre-taining: $200M — 1B+

Supervised Fine tuning: $5M — $20M

RLHF aligment: $5M — $50M

Along with it months of training , thousands of GPU and armies of human reviewers are required.

What if you could skip this expensive parts??

Model Distillation

Model distillation (often called knowledge distillation) is a technique in machine learning where a smaller model is trained to imitate a larger, more powerful model. The goal is to keep most of the performance while making the model faster, cheaper, and lighter.

Analogy: giving a trainee photographer the edited highlights from a professional’s photo collection

Other analogy which we can refer is “teacher taking notes from the textbook”. Notes are not quite deep as the really good for reading and getting good grades.

Disadvantage:

• Smaller, faster, and cheaper but may be less accurate, flexible and inherits teachers flaws
• Consist of compressed knowledge and not quite capable as origin model

🧠 Why Distillation Works

Distillation helps a small model learn not just the correct answer but how a powerful model reasons about it. Instead of just matching labels, it mimics the teacher’s internal distribution, which often encodes richer knowledge and patterns.

This makes:

• smaller models more accurate than training from scratch,
• easier transfer of advanced skills like reasoning and coding.

Distillation has changed everything:

• 100X cheaper to build models
• Small model but big capability
• Open Source explosion: Deep Seek, Llama all are distilled from frontier models

But unsurprisingly, distillation is against the terms of service of every major AI model we use everyday.

But Why this is controversial??

Distillation by itself is standard technique and widely used in research to compress big models in small. But the allegation focuses on:

• Unauthorized access to proprietor model,
• Violating access limits and usage restriction,
• Industrial scale extraction of capabilities rather than normal research use.

Legitimate vs Illicit Distillation:

Legitimate Distillation

Used by:

• research labs to make efficient models for real products,
• companies to compress large models for mobile use,
• academic experiments to transfer specific skills.

Example: Training the student model on output under clean data governance, train a large teacher model( carefully curated)

Illicit Distillation

Alleged used are:

• Making fake account to generate huge volume of outputs,
• Extracting internal behaviors from model (eg: claude),
• Bypassing regional limitation to access the target model.

Note: Chinese labs do use distillation for legitimate model compression and performance improvement as part of their research pipelines — for example, refining large models like DeepSeek‑R1 into smaller versions by training on outputs from those larger models or by using open‑source teacher models.

However, the industrial‑scale extraction from a competitor’s closed system that Anthropic describes — if accurate — represents a different scenario than standard academic or engineering distillation