knowledge-distillation

Techniques for transferring capabilities from a large teacher model into a smaller student, covering training setups, loss functions, and the practical tradeoffs of compression. Content here examines what survives the transfer and what quietly breaks: benchmark scores and surface outputs copy easily, while calibration, robustness, and reasoning faithfulness often degrade. Expect a critical look at when distillation earns its efficiency gains and when it silently ships a less trustworthy model.

knowledge-distillation, tacit-knowledge, model-evaluation, ai-expertise, publishing-expertise

What Actually Gets Distilled: Copying the Method Is Not Copying the Judgment

Knowledge distillation reliably copies a model's outputs and benchmark scores while losing the properties that made them trustworthy: calibration, robustness, and reasoning faithfulness. The same split applies to human expertise absorbed by AI systems -- your explicit method transfers cleanly, but y