The Sequence Knowledge #902: Learning About Distillation: When the Dataset Becomes the Teacher
TheSequence Jesus Rodriguez
Large language models can now generate synthetic training data for smaller models to learn from, shifting machine learning from excavating existing datasets to having capable models create custom learning experiences. The key mechanism involves running an expensive model offline to produce questions, answers, and explanations that become a dataset for training a smaller student model. This approach reduces production costs by eliminating the need for the large model at inference time while transferring some of its capabilities to the lighter student model.
Why it matters
Once models can generate their own curricula, data stops being a static resource and becomes a transmission medium for intelligence.
