Knowledge Distillation vs. Training on Synthetic Data - Understanding Two Ways AI Learns from AI

AI, LLM, Knowledge Distillation, Synthetic Data, Machine Learning, Terms of Service

Sat Mar 29 2025

The world of Large Language Models (LLMs) is rapidly evolving, and so are the techniques used to train them. Building powerful models from scratch requires immense data and computational resources. To overcome this, developers often leverage the knowledge contained within existing models. Two popular approaches involve using one AI to help train another: Knowledge Distillation and Training on Synthetically Generated Data.

While both methods involve transferring "knowledge" from one model (often larger or more capable) to another, they work in fundamentally different ways. Let's break down the distinction.

What is Knowledge Distillation (KD)?

Think of Knowledge Distillation as an apprenticeship. You have a large, knowledgeable "teacher" model and a smaller "student" model. The goal is typically to create a smaller, faster model (the student) that performs almost as well as the large teacher model.

How it works: The student model doesn't just learn from the correct answers (hard labels) in a dataset. Instead, it's trained to mimic the output probabilities (soft labels) produced by the teacher model for the same input data. Sometimes, the student also learns to match the teacher's internal representations.
The Core Idea: The teacher model's probability distribution across all possible outputs provides richer information than just the single correct answer. It reveals how the teacher "thinks" about the input and how certain it is about different possibilities. The student learns this nuanced reasoning process.
Analogy: A master chef (teacher) doesn't just tell the apprentice (student) the final dish (hard label); they show the apprentice how to mix ingredients and control the heat at each step (soft labels/internal process).
Goal: Primarily model compression and transferring complex capabilities to a more efficient model.

What is Training on Synthetic Data Generated by Another LLM?

This approach is more like using one author's published works to teach another writer. Here, one LLM (the "generator") creates entirely new data points, which are then used to train a different LLM (the "learner").

How it works: The generator model is prompted to produce text, code, question-answer pairs, dialogue, or other data formats relevant to the desired task. This generated output becomes the training dataset for the learner model. The learner model treats this synthetic data just like it would treat human-created data, typically using standard supervised fine-tuning methods.
The Core Idea: The generated data encapsulates patterns, knowledge, styles, or specific skills (like instruction following, often seen in "Self-Instruct" methods) present in the generator model. The learner model ingests these examples to acquire those capabilities.
Analogy: A historian (generator) writes several books (synthetic data). A student (learner) reads these books to learn about history, absorbing the facts, narratives, and style presented. The student isn't learning how the historian decided which words to use in real-time, but rather learning from the finished product.
Goal: Data augmentation (creating more training examples), bootstrapping capabilities (especially for instruction following), fine-tuning for specific styles or domains, or creating specialized datasets.

Key Differences Summarized

Feature	Knowledge Distillation	Training on Synthetic Data
Input for Learner	Same dataset as Teacher	New dataset generated by Generator
Learning Signal	Teacher's output probabilities (soft labels) or internal states	Generated data points (hard labels)
Mechanism	Mimicking Teacher's reasoning process	Learning from Generator's output examples
Primary Use	Model compression, capability transfer	Data augmentation, bootstrapping skills

Why Does the Distinction Matter?

Understanding the difference helps in choosing the right technique for your goal. If you need a smaller, faster version of an existing large model, Knowledge Distillation is often the way to go. If you need more training data for a specific task, style, or capability (like following instructions), generating synthetic data with a capable LLM can be highly effective.

An Important Note on Terms of Service

While leveraging existing models is powerful, it's crucial to be aware of the usage policies associated with the models you use, especially commercial ones.

Crucially, OpenAI's Terms of Use explicitly prohibit using the output from their services (including models like ChatGPT via the API or consumer interfaces) to develop AI models that compete with OpenAI.