Stanford Researchers Introduce Speculative Self-Distillation for LLM Fine-Tuning
The non-obvious insight is that you do not have to choose between on-policy accuracy and training cost — selective teacher intervention gets both, plus measurably less forgetting. Developers and teams doing domain adaptation or continued fine-tuning can use SSD-style distillation to add new knowledge without degrading existing capabilities at a fraction of the token budget. Anyone benchmarking against standard on-policy distillation pipelines should compare training time and retention metrics directly.
