Revisiting Transformer Layer Parameterization Through Causal Energy Minimization

TL;DR

This paper introduces Causal Energy Minimization (CEM), a novel framework that interprets Transformer layers as energy-based optimization steps, enabling new layer design insights and stable training at moderate scales.

Contribution

CEM provides a new perspective on Transformer layer parameterization by framing them as energy minimization processes, connecting architectures to energy-based models and exploring new design spaces.

Findings

CEM-derived layers train stably at moderate scales.

They can match baseline Transformer performance despite constrained parameterizations.

CEM offers a new lens for understanding and designing Transformer architectures.

Abstract

Transformer blocks typically combine multi-head attention (MHA) for token mixing with gated MLPs for token-wise feature transformation, yet many choices in their parameterization remain largely empirical. We introduce Causal Energy Minimization (CEM), a framework that recasts Transformer layers as optimization steps on conditional energy functions while explicitly accounting for layer parameterization. Extending prior energy-based interpretations of attention, CEM shows that weight-tied MHA can be derived as a gradient update on an interaction energy, and that a gated MLP with shared up/down projections can be viewed through an element-wise energy. This perspective identifies a design space for Transformer layers that includes within-layer weight sharing, diagonal-plus-low-rank interactions, lightweight preconditioners, and recursive updates. We evaluate CEM-derived layers in language-modeling experiments at the moderate hundred-million-parameter scale. Despite their constrained parameterizations, these layers train stably and can match corresponding Transformer baselines. Overall, our results suggest that CEM provides a useful lens for understanding Transformer layer parameterization, connecting Transformer architectures to energy-based models and motivating further exploration of energy-guided layer designs.