What is Knowledge Distillation?

Knowledge distillation is a machine learning technique used to transfer knowledge from a large, complex model (called the teacher) to a smaller, simpler model (called the student). Instead of training the student on the original dataset (with “hard” labels), the student learns from the soft outputs (probability distributions) of the teacher.

These soft outputs carry more nuanced information about the teacher’s understanding of the data. For example, how confident it is in its predictions and how it sees relationships between classes. This makes the student model nearly as accurate as the teacher while being more efficient in terms of speed and resource usage.

Knowledge distillation is also known as “neural network distillation”.

Knowledge distillation is often used for:

• Deploying models on edge devices or mobile phones where memory and compute power are limited.
• Speeding up inference while retaining high accuracy. For example, for real-time applications like chatbots.

For example, a deep neural network trained for image classification that achieves 95% accuracy is too heavy to run on a smartphone. Instead, a smaller neural network can be trained using the larger model’s output probabilities as training targets. This means the smaller one “mimics” the larger model’s behavior, often achieving similar performance at a fraction of the computational cost.

How Knowledge Distillation Works in LLMs

Here’s a step-by-step explanation of how LLM knowledge distillation works:

1. Training the Teacher Model – A powerful LLM (e.g., GPT-4, Gemini) is trained on a massive dataset.
2. Generating Soft Targets – The teacher processes training data and outputs “soft labels”. These are probability distributions over possible outputs (e.g., next word predictions), not just the final answer. These soft outputs contain richer information than hard labels (like just the correct class or word), capturing uncertainty and nuanced relationships between tokens.
3. Training the Student Model – A smaller model is trained to mimic the teacher. The student uses the teacher’s soft labels as supervision. It learns not just to be correct but to behave like the teacher in how it arrives at answers. The original training data might also be used.
4. Loss Functions Used – The student model is optimized using a loss function that compares its output distribution to the teacher’s output. A common choice is Kullback–Leibler divergence or traditional loss on ground truth labels (cross-entropy).

Knowledge Distillation Types

There are three main types of Knowledge Distillation:

1. Response-based Knowledge Distillation

In this type of Knowledge Distillation, the student model mimics the teacher’s predictions based on the final output layer. This is done by minimizing the distillation loss with a loss function. This loss is reduced over time.

2. Feature-based Knowledge Distillation

In feature-based knowledge distillation, the student mimics feature activations from the teacher’s intermediate and output layers. This is done by the loss function by minimizing the differences teacher’s and student’s feature activations.

3. Relation-based Knowledge Distillation

The third type of Knowledge Distillation is based on the relationships between the data samples and layers. It correlates feature maps, graphs, similarity matrix, feature embeddings, or probabilistic distributions based on feature representations. The student either distills information after the teacher model has been trained, at the same time the teacher model is being trained or by using the same network as the teacher. 

What are the Advantages of Knowledge Distillation?

Knowledge distillation offers several key advantages for real-world applications. Here are the main benefits:

• Model Compression Without Major Performance Loss – Knowledge distillation allows a smaller, faster “student” model to perform tasks similarly to a large, highly accurate, but expensive, “teacher” model, with a much lower computational footprint. This is especially important for edge devices with limited memory and processing power.
• Faster Inference and Lower Latency – By training smaller student models to replicate the teacher’s behavior, models are much quicker at inference time. This helps reduce response times in user-facing applications and enables scaling to more users without increasing infrastructure cost. This is especially important for real-time applications like chatbots or autonomous systems.
• Energy and Cost Savings – Smaller models require less hardware to train and run. This translates into reduced energy consumption, carbon footprint and operating costs.
• Better Generalization – Since the student is trained using “soft labels” (i.e., the teacher’s probability distributions over classes), it learns more nuanced decision boundaries than it would from hard labels alone. This often results in better generalization to unseen data, especially in small-data regimes.
• Privacy and Security Benefits – In some use cases, the original training data may be sensitive (e.g., medical or financial data). Instead of sharing the data, an organization can distill the model and share the student. This allows others to use a performant model without access to the underlying data.

What are the Limitations of Knowledge Distillation?

Knowledge distillation is a powerful technique, but it has several limitations that are important to consider:

• Performance Gap – The student model might fail to match the teacher’s performance, especially on complex tasks. This is more noticeable when the student model is significantly smaller than the teacher.
• Dependency on Teacher Quality – If the teacher is biased, overfitted, or not well-generalized, the student will likely inherit those issues. Garbage in, garbage out.
• Training Complexity – The two-stage training process increases the overall training time and computational cost.

• Overfitting to Teacher – The student might learn to mimic the teacher too closely, rather than learning from the data distribution itself. This can lead to poor generalization, especially when the teacher overfits or captures dataset-specific noise.
• Accurate Tuning Difficulties – Knowledge distillation requires careful balancing of loss components (e.g., temperature, soft vs. hard label loss). Small tweaks can lead to significant changes in results, making it hard to fine-tune effectively.
• Limited Transferability – Knowledge distilled from one domain or task may not transfer well to another, especially in multitask or multi-domain settings. The distilled model can struggle to generalize if it wasn’t trained with task diversity in mind.
• Unclear ROI – In some cases, compression gains are minimal, especially when the student already performs well or when the gap between student and teacher isn’t large. You might end up adding training costs for very marginal runtime improvements.

When to Use Knowledge Distillation 

Knowledge distillation is valuable when there’s a need to deploy high-performing models in resource-constrained environments. Here are some of its most impactful applications:

• Model Compression for Edge and Mobile Devices – High-performing models like large language models or deep CNNs are often too heavy for devices with limited compute power, memory, or energy capacity. For example, distilling a large BERT model into MobileBERT for on-device NLP tasks, or deployment of lightweight computer vision models on edge devices (like phones or cameras) without significantly compromising accuracy.
• Faster Inference in Production – Latency-sensitive applications, like search engines, chatbots, or recommendation systems, require  much smaller and faster models, while maintaining competitive accuracy.
• Knowledge Transfer in Multi-Task and Continual Learning – Distillation helps transfer knowledge from a model trained on one task to another model targeting a related (but different) task.
• Ensemble Distillation – Rather than deploying an ensemble of models, data engineers can  distill their collective knowledge into a single, more efficient model. This keeps the high accuracy but dramatically reduces compute and storage needs.
• Regulated Industries – In sensitive domains like healthcare or finance, direct access to training data might be restricted. Distillation enables training student models from teacher models that were trained on private data, without ever accessing the raw data.
• Reinforcement Learning (RL)  – Knowledge distillation helps compress policies or value functions learned by complex RL agents into smaller models. This is useful for deploying RL agents in real-world scenarios like robotics, where compute resources are limited.