Nemotron-Labs-Diffusion-Uni: Elastic Self-Refining Unified Diffusion LLM with Cross-Paradigm On-Policy Distillation
NLD-Uni is presented as a unified discrete diffusion large language model for visual understanding and generation. It introduces a cross-paradigm on-policy distillation framework that transfers visual understanding from autoregressive VLMs and visual generation from continuous flow models into a unified DLLM. It also uses a progressive self-refining diffusion process that gradually corrupts tokens across steps, rather than masking them directly, allowing intermediate token commits at inference. The authors report state-of-the-art results among unified DLLMs on visual question-answering, multimodal reasoning, object grounding, text-to-image generation, and image editing.
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NLD-Uni is presented as a unified discrete diffusion large language model for visual understanding and generation. It introduces a cross-paradigm on-policy distillation framework that transfers visual understanding from autoregressive VLMs and visual generation from continuous flow models into a unified DLLM. It also uses a progressive self-refining diffusion process that gradually corrupts tokens across steps, rather than masking them directly, allowing intermediate token commits at inference. The authors report state-of-the-art results among unified DLLMs on visual question-answering, multimodal reasoning, object grounding, text-to-image generation, and image editing.
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Key findings from the stored brief
- Cross-paradigm on-policy distillation transfers understanding from autoregressive VLMs and generation from continuous flow models into one DLLM.
- Progressive self-refining diffusion corrupts tokens gradually over steps instead of directly replacing them with mask tokens.
- This design lets the model make intermediate token commits at inference, enabling progressive self-refinement.
- The authors report state-of-the-art performance among unified DLLMs across multiple visual and multimodal tasks.
Source claims not independently checked; the reported state-of-the-art results are the authors' own claims from their experiments.
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Why this may matter
Unifying understanding and generation in one diffusion language model could simplify multimodal systems. The training and inference changes are concrete design choices that others could evaluate against existing unified models.
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