Large language models (LLMs) are changing how artificial intelligence is built. Instead of training from scratch on endless data, companies now use a technique called model distillation. This method helps smaller, faster models learn from bigger, more powerful ones. The goal is to keep the impressive abilities of large models while making them easier and cheaper to deploy.

What Is LLM Distillation?

LLM distillation involves transferring knowledge from a large, pre-trained model (the teacher) to a smaller, more efficient model (the student). The teacher model has learned a lot from massive datasets, and the student learns by mimicking its output or internal reasoning. This process can happen during initial training or after a model is fully trained.

There are three main ways to do this. The first is soft-label distillation, where the student learns from the probabilities the teacher assigns to each possible next word. The second is hard-label distillation, where the student only looks at the final answer the teacher produces. The third is co-distillation, where both models learn together and influence each other during training.

Soft-Label Distillation Explained

This method involves the teacher model providing a full probability distribution over all possible next tokens. For example, instead of just saying the next word is “cat,” the teacher might say there’s a 70% chance it’s “cat,” 20% for “dog,” and 10% for “animal.” The student then learns not just the correct answer but also the relationships and uncertainties between different options. This richer information helps smaller models develop better reasoning and understanding.

The main advantage is that the student can inherit many capabilities of the larger model, like reasoning and instruction following, while remaining faster and less costly to run. However, soft-label distillation requires access to the teacher model’s internal data, which isn’t always possible with proprietary models. Also, storing full probability distributions for huge vocabularies can be very resource-intensive.

Hard-Label Distillation in Practice

Hard-label distillation is simpler. Here, the teacher model just provides its final answer for each input. The student then trains to produce the same output. This is less demanding because it doesn’t need the internal probabilities—just the final answer. It’s also useful when using black-box models like APIs where only the output text is accessible.

While it provides less detailed information than soft labels, this method is still very effective. It works well for fine-tuning models on specific tasks, like answering questions or generating structured data. It’s also more practical for many real-world applications due to lower resource needs.

Co-distillation: Learning Together

Co-distillation involves training the teacher and student models together. Both models process the same data at the same time, each generating their own predictions. The teacher is trained on standard data, while the student learns by trying to match the teacher’s outputs. This method allows both models to improve simultaneously, with the student catching up to the teacher’s knowledge.

A challenge here is that early in training, the teacher’s predictions might be noisy. To address this, the training combines the usual correct answers with the teacher’s softer predictions. Over time, the student becomes more accurate and can even surpass the teacher in some cases. This collaborative approach can lead to more efficient and robust models.

In summary, model distillation is a key tool for developing smarter, faster, and more accessible AI systems. By sharing knowledge between models, researchers can build AI that performs well without needing enormous computational resources. As this technique advances, expect to see more capable AI systems that are easier to implement across various applications.

Inspired by

• https://www.marktechpost.com/2026/05/11/understanding-llm-distillation-techniques/