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.
By Taiming Lu, Zhuang Liu
arXiv:2606. 31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.
By Gaurab Baral, Aaditya Khanal, Yangyang Tao, Junxiu Zhou
The paper presents a method for distilling tabular foundation models (TFMs) into lightweight, dataset‑specific students. By using the full labeled training set as teacher context and training students on both observed and synthetic queries, the authors achieve significant performance gains over traditional supervised models on TabArena and TALENT benchmarks. The distilled students also provide substantial inference speedups, reducing the cost of repeated inference.
By Minho Jeong, Dooho Lee, Jinmo Lee, Jaemin Yoo
KDFP is a new white‑box methodology for general knowledge distillation in large language models, addressing a gap left by prior work that focused mainly on post‑training abilities such as instruction following and chain‑of‑thought reasoning. The authors evaluate lessons from earlier studies and conduct new experiments to design a distillation approach tailored to modern LLMs. KDFP achieves 1.6%–4.9% higher performance across nine benchmarks and improves training efficiency by up to 99.1% through reduced temporary parameters.
By Ryan Swift, Konstantinos Psounis
arXiv:2603. 01875v3 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones.
By Songming Zhang, Xue Zhang, Tong Zhang, Bojie Hu, Yufeng Chen, Jinan Xu
The paper introduces TALON, a Task‑Adaptive LoRA‑Teacher framework for Few‑Shot Class‑Incremental Learning. TALON assigns a dedicated LoRA‑Teacher to each incremental task, then distills the frozen teachers into a single LoRA‑Student via Ensemble Knowledge Transfer, using a semantic‑guided weighting scheme to reduce forgetting and overfitting. Experiments on four FSCIL benchmarks show that TALON matches or surpasses state‑of‑the‑art accuracy while using up to 33× fewer deployment parameters and cutting inference time by 41.7%.
By Hongwei Zhao (School of Computer Science,Engineering, Beihang University), Rui Liu (School of Computer Science,Engineering, Beihang University), Yansong Liu (School of Computer Science,Engineering, Beihang University), Zhiyuan Zou (School of Computer Science,Engineering, Beihang University), Yong Chen (School of Computer Science, Beijing University of Posts,Telecommunications)