A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation
  through Low-Rank Clone
Authors: name: Jitai Hao, name: Qiang Huang, name: Hao Liu, name: Xinyan Xiao, name: Zhaochun Ren, name: Jun Yu

Training high-performing Small Language Models (SLMs) remains costly, even
with knowledge distillation and pruning from larger teacher models. Existing
work often faces three key challenges: (1) information loss from hard pruning,
(2) inefficient alignment of representations, and (3) underutilization of
informative activations, particularly from Feed-Forward Networks (FFNs). To
address these challenges, we introduce Low-Rank Clone (LRC), an efficient
pre-training method that constructs SLMs aspiring to behavioral equivalence
with strong teacher models. LRC trains a set of low-rank projection matrices
that jointly enable soft pruning by compressing teacher weights, and activation
clone by aligning student activations, including FFN signals, with those of the
teacher. This unified design maximizes knowledge transfer while removing the
need for explicit alignment modules. Extensive experiments with open-source
teachers (e.g., Llama-3.2-3B-Instruct, Qwen2.5-3B/7B-Instruct) show that LRC
matches or surpasses state-of-the-art models trained on trillions of
tokens--while using only 20B tokens, achieving over 1,000x training efficiency.
Our codes and model checkpoints are available at
https://github.com/CURRENTF/LowRankClone and
https://huggingface.co/collections/JitaiHao/low-rank-clone-lrc-6828389e96a93f1d4219dfaf.