
Source: Research paper titled: “Knowledge Distillation: A Survey
Background and Motivation
In January of 2025, DeepSeek’s R1 model took the AI research community by storm and emerged as a powerful player in the development of efficient and scalable language models. Known for its cutting-edge research, DeepSeek uses Knowledge Distillation to shrink large, high-performing models into smaller, faster, and more cost-effective versions without significant loss of accuracy. The impact of DeepSeek R1 was so compelling that it led to a tech stock selloff, with Nvidia’s stock plunging nearly 17% (~$600 billion in market value) and the Nasdaq dropping by 3.1% (Reuters).
So, what is Knowledge Distillation?
Also known as Model Distillation, Knowledge Distillation is a deep learning technique that transfers knowledge from a large, complex model (the teacher) to a smaller, more efficient model (the student). The goal of this process is to retain as much of the predictive power of the large model as possible while reducing computational cost. Ultimately, this makes the smaller model more suitable for deployment on edge devices, mobile applications, and scenarios with limited computational resources, as the models become easier to fine-tune both in terms of cost and time.
Knowledge Distillation Overview
History
Introduced by Geoffrey Hinton and his colleagues in their 2015 paper “Distilling the Knowledge in a Neural Network,” (Read the paper here), Knowledge Distillation typically involves training the student model to learn the behavior of the teacher model. This process helps the student model learn more nuanced patterns and generalize better.
Before Knowledge Distillation, the traditional method of training models involved using labeled datasets with hard class labels. For example, an image of a dog would be strictly labeled as “dog” without additional context. However, this approach does not capture the subtle relationships between different classes. In Knowledge Distillation, instead of relying solely on these hard labels, the teacher model generates soft labels, which are probability distributions over all possible classes.
For instance, given an image of a Dachshund (a dog breed type), a standard classification model might assign it a label of “dog” with 100% certainty. However, a teacher model trained on a richer dataset might output the following probabilities: [Dog: 85%, Wolf: 10%, Cat: 5%]

Source: Medium
These soft labels provide a more nuanced understanding of the input, as they reveal how the teacher model perceives relationships between different categories. The student model is then trained to mimic these probability distributions rather than just memorizing discrete label. This allows it to learn richer representations from the teacher’s knowledge.
Knowledge Distillation
Compared to traditional model training, where the goal is to develop a model that fits or predicts data, Knowledge Distillation shifts the objective. Instead of learning from raw data alone, the student model is trained to mimic a larger, more complex model. This approach is particularly effective in resource-constrained environments, such as mobile devices and embedded systems, where deploying a full-scale model would be computationally impractical. It is also widely used in real-time applications like chatbots and recommendation systems, where latency and efficiency are critical.
Additionally, Knowledge Distillation is beneficial for privacy-sensitive applications, such as healthcare and finance. In these contexts, training a new model from scratch on sensitive data may not be feasible. Instead, a pre-trained model can distill knowledge without direct exposure to the underlying data. By leveraging this technique, organizations like DeepSeek can maintain high-performance AI models while ensuring scalability, efficiency, and broader accessibility.
Another key advantage of Knowledge Distilled models is their potential to enhance model explainability (Han et al.). According to the paper by Han et al., Knowledge Distillation not only improves model accuracy but also enhances interpretability by transferring class-similarity information from teacher to student models. This transfer enables student models to better understand and represent the relationships between classes, making their decision-making processes more transparent.
Teacher Student Approach
The teacher-student approach in Knowledge Distillation allows a smaller model to efficiently learn from a larger, more complex model by transferring its knowledge in a structured manner. The process begins by training a teacher model, typically a large and highly accurate neural network, on a labeled dataset. An alternative to this is obtaining access to a larger model that already exists. Once trained, the teacher generates soft labels, which are probability distributions over all possible classes rather than just a single prediction. These soft labels are then converted to hard labels either based on max probability or sampling.
Temperature Scaling
To make these outputs more informative, a technique called temperature scaling is applied. Here, a temperature parameter controls the randomness of the model’s output. A higher temperature smooths the probability distribution, making class probabilities more uniform and revealing relationships between them. Conversely, a lower temperature sharpens the distribution, increasing confidence in the top prediction by suppressing lower-probability classes.
In Knowledge Distillation, smoothing the probabilities with a higher temperature is important because it reveals relationships between classes. This helps the student model, a smaller and more efficient network, learn more nuanced information between classes. The student model is then trained using these soft labels alongside standard hard labels, allowing it to retain essential knowledge from the teacher while improving generalization.

Source: Intellabs GitHub
In the above image, you can see the last and penultimate layer of a neural network. Note that the second-to-last layer produces a probability distribution over possible classes using the softmax function. The temperature parameter T influences this. The final layer then collapses this information into a single hard prediction, discarding valuable details. By training on the softened probabilities from the teacher’s second-to-last layer (since a lot of information gets lost in the last layer), the student model learns more nuanced knowledge.
Teacher Student Approach: Objective
The Teacher Student approach’s objective is to minimize the difference between the student’s predictions and the teacher’s outputs. It often uses a combination of Kullback-Leibler (KL) divergence (for soft labels) and cross-entropy loss (for hard labels).
In simple terms, KL Divergence measures how much one probability distribution differs from another. In Knowledge Distillation, it ensures the student model’s output distribution closely matches the teacher’s soft labels. Meanwhile, cross-entropy loss is the standard way to train classification models using hard labels. It ensures that the student model correctly classifies inputs by giving higher penalties when the wrong class is predicted with high confidence. By combining KL divergence and cross-entropy loss, the student model not only learns the teacher’s general behavior but also remains accurate in making final predictions.
Synthetic Data Generation
In Knowledge Distillation, synthetic data generation plays a crucial role in enhancing the training of student models. This is particularly effective in cases where real-world data is limited, sensitive, or difficult to acquire. We can create synthetic data by either augmenting real data or using the teacher model to generate new training examples. This helps the student model generalize better and learn patterns that may not be well-represented in the original dataset.
Methods for synthetic data generation include:
- Data Augmentation: Simple transformations such as rotations, translations, noise addition, or paraphrasing (in NLP) create variations of existing data. This allows the student to learn more robust features.
- Model-Based Synthetic Data: The teacher model can generate pseudo-labeled data to train the student model. Example: In self-distillation, the same model generates labels for new data, improving performance without requiring external annotations.
- Generative Models: Techniques like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) create entirely new, realistic training samples, ensuring better diversity in the dataset.
Synthetic data is particularly useful in cases like privacy-preserving machine learning (e.g., federated learning) or low-resource domains where labeled data is hard to obtain. By incorporating synthetic data generation, Knowledge Distillation enables student models to learn more effectively, improving their performance without increasing dataset requirements.
Challenges and Limitations
While Knowledge Distillation is a powerful technique for model compression, it comes with several limitations.
- Loss of Information: Since the student model is much smaller than the teacher, it cannot capture all the knowledge embedded in the larger model. We could lose some intricate patterns and complex reasoning capabilities during distillation, leading to a drop in performance.
- Computational Costs of Training the Teacher: Training a high-quality teacher model for this process requires substantial computing power. This makes Knowledge Distillation impractical for organizations with limited resources. Additionally, if the teacher model is not well-trained, the student model inherits its weaknesses. To mitigate these costs, many researchers and organizations use pre-trained models as teachers rather than training from scratch. However, using proprietary models as teachers raises concerns about licensing restrictions, API costs, and dependency on external providers. In contrast, open-source alternatives, provide more flexibility but may require fine-tuning to align with specific use cases.
- Sensitivity to Temperature and Loss Balancing: The choice of temperature scaling and how KL divergence and cross-entropy loss are weighted significantly impact the student’s learning. If these hyperparameters are not tuned properly, the student may fail to generalize well or overfit to the teacher’s biases.
- Ethical and Security Concerns: If the teacher model has learned biases or sensitive information from the training data, those biases can be transferred to the student model. Additionally, distilling from proprietary models may raise concerns about intellectual property and data security.
Videos for Further Exploration
- “Knowledge Distillation in Deep Neural Network” provides a great overview of how Knowledge Distillation works, starting from Geoffrey Hinton’s paper (Runtime: 4 minutes).
- To get a better understanding of the Teacher Student approach and its implementation, check out “Teacher-Student Neural Networks: The Secret to Supercharged AI” (Runtime: 13 minutes).
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