Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability of Decoder-Only Transformer Models

TL;DR

This paper explores the complex, fractal-like boundaries between trainable and untrainable regions in decoder-only transformer models, revealing self-similar patterns and chaotic borders that influence training stability.

Contribution

It extends fractal boundary analysis from small neural networks to medium-sized transformers, providing new insights into their trainability landscape and hyperparameter sensitivity.

Findings

Trainability boundaries exhibit fractal, self-similar structures.

A chaotic border surrounds stable convergence regions.

Patterns in the hyperparameter landscape are statistically consistent.

Abstract

In the realm of fractal geometry, intricate structures emerge from simple iterative processes that partition parameter spaces into regions of stability and instability. Likewise, training large language models involves iteratively applying update functions, such as Adam, where even slight hyperparameter adjustments can shift the training process from convergence to divergence. Recent evidence from miniature neural networks suggests that the boundary separating these outcomes displays fractal characteristics. Building on these insights, this study extends them to medium-sized, decoder-only transformer architectures by employing a more consistent convergence measure and examining the learning rate hyperparameter landscape for attention and fully connected layers. The results show that the trainability frontier is not a simple threshold; rather, it forms a self-similar yet seemingly random structure at multiple scales, with statistically consistent and repeating patterns. Within this landscape, a region of stable convergence is surrounded by a complex chaotic border, illustrating the sensitive nature of the underlying training dynamics.