Knowledge distillation is a model-compression and capability-transfer technique in which a powerful teacher model provides training signals for a smaller student model.
Instead of requiring the student to rediscover every useful behaviour from raw internet-scale pretraining data, the student learns from the teacher’s outputs, probability distributions, internal representations, reasoning demonstrations or preferences.
A distilled model can therefore become:
- Faster at inference
- Less expensive to operate
- Smaller in memory
- Easier to deploy on limited hardware
- More specialised for a particular task
- More consistent than the original general-purpose model for a narrow workflow
However, an important distinction must be made:
The exact internal process used to train current proprietary Claude models is not publicly disclosed in full.
Anthropic publishes model reports, safety research and selected training information, but its public materials do not expose Claude’s weights, token logits, hidden states, training datasets or the complete teacher–student training recipe. Therefore, when people discuss “distilling Claude,” they may be referring to two different things:
- Authorised distillation within an official platform, such as Amazon Bedrock’s documented Claude Sonnet-to-Haiku distillation workflow.
- Black-box behavioural distillation, where permitted Claude outputs are collected and used to fine-tune another model.
Anthropic and AWS have publicly described an authorised workflow in which Claude 3.5 Sonnet generates synthetic training data, Claude 3 Haiku is trained and evaluated using that data, and the resulting distilled model is hosted for inference. This is primarily output-based behavioural distillation rather than traditional access to Claude’s internal logits or hidden layers.