A recently proposed method enables efficient estimation of the KL divergence between language models, including models with different architectures, by assigning coordinates based on log-likelihood vectors. To better understand the behavior of this metric, we systematically evaluate KL divergence across a wide range of conditions using publicly available language models. Our analysis covers comparisons between pretraining checkpoints, fine-tuned and base models, and layers via the logit lens. We find that trajectories of language models, as measured by KL divergence, exhibit a spiral structure during pretraining and thread-like progressions across layers. Furthermore, we show that, in terms of diffusion exponents, model trajectories in the log-likelihood space are more constrained than those in weight space.
View on arXiv@article{kishino2025_2505.15353,
title={ Revealing Language Model Trajectories via Kullback-Leibler Divergence },
author={ Ryo Kishino and Yusuke Takase and Momose Oyama and Hiroaki Yamagiwa and Hidetoshi Shimodaira },
journal={arXiv preprint arXiv:2505.15353},
year={ 2025 }
}