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arXiv Computation and Language By Taiming Lu, Zhuang Liu

Strong Teacher Not Needed? On Distillation in LLM Pretraining

Read the original on arXiv Computation and Language →

The paper investigates whether a strong teacher is necessary for knowledge distillation in large language model pretraining. By varying teacher and student sizes and token budgets, the authors create strong‑to‑weak, same‑level, and weak‑to‑strong teacher‑student pairs and evaluate distillation effectiveness. They find that even small, undertrained teachers can improve larger students when losses are properly mixed, and that pushing teachers to be stronger can saturate or reverse gains, while distillation mainly boosts generalization rather than in‑domain fitting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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