AI RESEARCH
Learned Relay Representations for Forward-Thinking Discrete Diffusion Models
arXiv CS.LG
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ArXi:2605.22967v1 Announce Type: new When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, forcing every subsequent refinement step to recompute the valuable internal information d as model representations. To avoid a hard reset between denoising rounds, we propose Learned Relay Representations (Relay), a method that allows MDMs to be forward-thinking when denoising by explicitly learning how to propagate latent information for the benefit of future denoising steps. Relay.