Layer Collapse in Diffusion Language Models
Diffusion language models (DLMs) have recently emerged as competitive alternatives to autoregressive (AR) language models, yet differences in their activation dynamics remain poorly understood. We characterize these dynamics in LLaDA-8B and identify a striking layer-collapse property: a few early layers exhibit highly similar, collapsed activation patterns dominated by a single large super-outlier persisting over a long token range. Despite its apparent redundancy, this outlier is critical: pruning it causes outputs to degrade into repetitive random token loops. Paradoxically, layers in LLaDA contain more redundant representations overall, with redundancy most pronounced in earlier layers...
| Authors | Alexander Conzelmann, Albert Catalan-Tatjer, Shiwei Liu |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 11 May 2026 |
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