Distillation techniques for large language models

Knowledge distillation has become one of the most important techniques for making large language models practical. The basic idea is simple: train a smaller "student" model to mimic the behavior of a larger "teacher" model. But the details matter enormously, and recent advances have made this process much more effective.

why distillation matters now

As models grow to hundreds of billions of parameters, the gap between what is possible in research labs and what is deployable in production widens. Distillation bridges this gap by transferring the knowledge encoded in massive models into compact ones that can run on consumer hardware. The key insight from recent work is that task-specific distillation dramatically outperforms general-purpose distillation.

three approaches to try

Logit distillation — The classic approach: minimize KL divergence between teacher and student output distributions. Works well for classification tasks.
Chain-of-thought distillation — Train the student to reproduce the teacher's reasoning process, not just the final answer. Especially effective for math and logic tasks.
Selective distillation — Only distill on examples where the student disagrees with the teacher. More data-efficient and avoids redundant training.

In our lab, we have found that combining approaches 2 and 3 yields the best results for NLP tasks. The student model not only learns to produce correct answers but also develops transferable reasoning patterns that generalize beyond the distillation data.