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Modern large language models are no longer trained solely on raw internet text. Companies increasingly use powerful “teacher” models to help train smaller or more efficient “student” models through a technique known as LLM distillation. This has become a key method for building high-performing models at lower computational costs. For example, Meta used its massive Llama 4 Behemoth model to assist in training Llama 4 Scout and Maverick, while Google leveraged Gemini models during the development of Gemma 2 and Gemma 3. Similarly, DeepSeek distilled reasoning capabilities from DeepSeek-R1 into smaller Qwen and LLM-based models.
Soft-Label Distillation
Soft-label distillation is a training technique where a smaller student LLM learns by imitating the output probability distribution of a larger teacher LLM. Instead of training only on the correct next token, the student is trained to match the teacher’s softmax probabilities across the entire vocabulary. This allows smaller models to inherit capabilities such as reasoning and structured generation from much larger systems. The biggest advantage of soft-label distillation is that it enables smaller models to learn richer signals about relationships between tokens, making them faster and cheaper to deploy. However, this method also comes with practical challenges: generating soft labels requires access to the teacher model’s logits or weights, which may not be available for closed-source models, and storing probability distributions for massive vocabularies is memory-intensive.
Hard-Label Distillation
Hard-label distillation simplifies the process by having the student LLM learn only from the teacher model’s final predicted output token. In this setup, a pre-trained teacher generates synthetic training data for the student. DeepSeek used this approach to distill reasoning capabilities from DeepSeek-R1 into smaller Qwen and Llama 3.1 models. Unlike soft-label distillation, hard-label distillation is computationally much cheaper and easier to implement since it does not require storing massive probability distributions for every token.
Co-Distillation
Co-distillation combines both the teacher and student models in training. Both process the same data simultaneously, with the teacher being trained using ground-truth hard labels and the student learning by matching its soft labels along with actual correct answers. Meta used this approach while training Llama 4 Scout and Maverick alongside their larger Llama 4 Behemoth model. One challenge is that the teacher’s predictions are initially noisy or inaccurate during early stages, so a combination of soft-label distillation loss and standard hard-label cross-entropy loss is often used to stabilize learning.
Comparing the Three Distillation Techniques
Soft-label distillation transfers the richest form of knowledge by allowing smaller models to learn from the teacher’s full probability distribution. This helps them capture reasoning patterns and uncertainty, often leading to stronger overall performance but at a higher computational cost due to memory requirements for storing probability distributions.
Hard-label distillation is simpler and more practical. The student learns only from the teacher’s final generated outputs, making it much cheaper and easier to implement. It works especially well with proprietary black-box models like GPT-4 APIs where internal probabilities are unavailable. While this approach loses some of the deeper “dark knowledge” present in soft labels, it remains highly effective for instruction tuning, synthetic data generation, and task-specific fine-tuning.
Co-distillation takes a collaborative approach where both models learn together during training. This can reduce the performance gap seen with traditional one-way distillation methods but makes training more complex since the teacher’s predictions are initially unstable. In practice, soft-label distillation is preferred for maximum knowledge transfer, hard-label distillation for scalability and practicality, and co-distillation for large-scale joint training setups.
The post Understanding LLM Distillation Techniques appeared first on MarkTechPost.
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