Layer-wise representation alignment lets diffusion language models reuse semantic structures from frozen autoregressive models, accelerating training by up to 4x without architectural changes beyond the attention mask.
Gritsenko and Jasmijn Bastings and Ben Poole and Rianne van den Berg and Tim Salimans , title =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2representative citing papers
Absorbing discrete diffusion models the conditional distributions of clean data; reparameterizing yields a time-independent RADD that unifies with AO-ARMs and reaches SOTA perplexity among diffusion models on zero-shot language benchmarks.
citing papers explorer
-
Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment
Layer-wise representation alignment lets diffusion language models reuse semantic structures from frozen autoregressive models, accelerating training by up to 4x without architectural changes beyond the attention mask.
-
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
Absorbing discrete diffusion models the conditional distributions of clean data; reparameterizing yields a time-independent RADD that unifies with AO-ARMs and reaches SOTA perplexity among diffusion models on zero-shot language benchmarks.