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Generalized Interpolating Discrete Diffusion
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Generalized Interpolating Discrete Diffusion
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While state-of-the-art language models achieve impressive results through next-token prediction, they have inherent limitations such as the inability to revise already generated tokens. This has prompted exploration of alternative approaches such as discrete diffusion. However, masked diffusion, which has emerged as a popular choice due to its simplicity and effectiveness, reintroduces this inability to revise words. To overcome this, we generalize masked diffusion, deriving a new family of general interpolating discrete diffusion (GIDD) which offers greater flexibility in the design of the noising processes. Leveraging a novel diffusion ELBO, we achieve compute-matched state-of-the-art performance in diffusion language modeling. Exploiting GIDD's flexibility, we explore a hybrid approach combining masking and uniform noise, leading to improved sample quality and unlocking the ability for the model to correct its own mistakes, an area where autoregressive models notoriously have struggled. Code: https://github.com/dvruette/gidd/
Forward citations
Cited by 12 Pith papers
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Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner
CCDD defines a joint multimodal diffusion on continuous representation space and discrete token space to combine expressivity with explicit token supervision for diffusion language models.
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Hierarchical Domain Generalization
Over infinite domains, hierarchy-uniform domain generalization is impossible for every nontrivial hypothesis class; a length-generalization bound is a property of the length hierarchy, not a hierarchy-free guarantee.
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Progressive supervised block merging plus stage-wise intra-diffusion distillation converts Qwen3-VL into a large-block dVLM with claimed SOTA open-dVLM scores and up to 3× decoding speedup on ≤4.4M data.
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Fine-Tuning Masked Diffusion for Provable Self-Correction
PRISM fine-tunes any masked diffusion model with a binary-cross-entropy loss so its new head provably estimates per-token quality p(x_i=y_i|y⊕m_i) and can remask low-quality tokens at inference.
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DMax: Aggressive Parallel Decoding for dLLMs
DMax enables faster parallel decoding in diffusion language models by using on-policy training to recover from errors and soft embedding interpolations for iterative revision, boosting tokens per forward pass roughly ...
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Introduces a standardized evaluation setup and SQL-D1 agent for diffusion language models on NL2SQL, claiming structural robustness advantages over autoregressive models.
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Re-evaluating Confidence Remasking in Masked Diffusion Language Models
Re-evaluation finds post-hoc remasking (WINO) yields little-to-no gain over confidence unmasking in standard dLLM settings and can worsen diversity collapse under stochastic decoding.
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