Self-distillation explained: how a model teaches itself with richer token-level training signals
arpit_bhayani · x · 2026-10-28
Self-distillation is a variant where teacher and student are the same model: one copy gets extra context (ground-truth solution, failed test, compiler error) and acts as teacher, while the original learns to match its token-level outputs — a far richer training signal than right/wrong alone, and cheaper than an external larger teacher. The trade-off: if training rewards direct answers over verification behaviors like testing assumptions, the model may get more accurate while weakening its ability to handle unfamiliar problems. What the process rewards becomes critical.
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