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arXiv · 2610.02344

Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures

José Lucas De Melo Costa·Seong Woo Ahn·Fabrice Popineau·Arpad Rimmel·Bich-Liên Doan
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Abstract

Joint-Embedding Predictive Architectures (JEPAs) are prone to representation collapse, typically mitigated through empirical heuristics. We develop an early-training stability theory that unifies these heuristics. Linearising the coupled JEPA gradient flow around the trivial fixed point reveals two competing effects: a driving force ($γ$) and a decay effect ($σ$). Under approximate spectral decoupling, a per-mode stability ratio $μ_i = γ_i / σ_i$ factorises into independent data-side and predictor-side terms and the count of unstable modes tracks the rank of representations that can emerge. The framework predicts a phase boundary, which we confirm empirically across more than 800 Tabular-JEPA configurations. It also unifies predictor scaling, masking ratio, and EMA as distinct mechanisms for shifting $μ$. Guided by this analysis, we introduce ResidualPred, a transformer predictor whose attention is biased toward the identity at initialisation; it improves both effective rank and downstream accuracy on tabular benchmarks and in I-JEPA pretraining on CIFAR-10, CIFAR-100, STL-10, and ImageNet. Our framework connects empirical collapse-avoidance heuristics to an explicit dynamical picture, yielding theory-driven stabilizers. Code is available at https://github.com/jose-melo/drive-vs-decay.

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José Lucas De Melo Costa, Seong Woo Ahn, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan. 2026-10-01. Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures. https://arxiv.org/abs/2610.02344

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