Large Language Models (LLMs) are known for their performance, but we uncover a significant structural inefficiency: a phenomenon we term attention collapse. In many pre-trained decoder-style LLMs, the attention matrices in deeper layers degenerate, collapsing to near rank-one structures. These underutilized layers, which we call lazy layers, are redundant and impair model efficiency. To address this, we introduce Inheritune, a simple yet powerful training recipe designed to build smaller, stronger language models. Inheritune initializes a compact model by inheriting the potent early layers from a larger pre-trained model and then progressively trains and expands it. Our experiments on various models, including the GPT-2 family, demonstrate that models trained with Inheritune can match or even surpass the performance of their larger counterparts, despite having significantly fewer layers. This work presents a novel path toward model compression by design, enabling the creation of compact, yet highly performant language models. Code is available at https://github.com/sanyalsunny111/LLM-Inheritune.
@article{arxiv.2404.08634,
title = {When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models},
author = {Sunny Sanyal and Ravid Shwartz-Ziv and Alexandros G. Dimakis and Sujay Sanghavi},
journal= {arXiv preprint arXiv:2404.08634},
year = {2026}
}
Comments
Published in Transactions on Machine Learning Research (TMLR)