LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.
Di[M]O: Distilling masked diffusion models into one-step generator.arXiv preprint arXiv:2503.15457
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Theoretical analysis reveals MaskGIT's implicit temperature sampling in masked diffusion; proposes equivalent moment sampler and efficiency techniques for adaptive unmasking with image and text experiments.
BlockGen enables flexible blockwise diffusion modeling with mixed block sizes and ARPC sampling, finding uniform diffusion outperforms masked under ancestral sampling in few-step regimes while the gap reverses with ARPC at high NFE.
FP-MGMs with consistency loss and three-state reuse (CoFRe) reduce parameters by up to 38.8% and improve low-budget perplexity and FID versus standard masked generative models on text and images.
Coupling Models enable single-step discrete sequence generation via learned couplings to Gaussian latents and outperform prior one-step baselines on text perplexity, biological FBD, and image FID metrics.
citing papers explorer
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Large Language Diffusion Models
LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.
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Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
Theoretical analysis reveals MaskGIT's implicit temperature sampling in masked diffusion; proposes equivalent moment sampler and efficiency techniques for adaptive unmasking with image and text experiments.
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BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers
BlockGen enables flexible blockwise diffusion modeling with mixed block sizes and ARPC sampling, finding uniform diffusion outperforms masked under ancestral sampling in few-step regimes while the gap reverses with ARPC at high NFE.
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Fixed-Point Masked Generative Modeling
FP-MGMs with consistency loss and three-state reuse (CoFRe) reduce parameters by up to 38.8% and improve low-budget perplexity and FID versus standard masked generative models on text and images.
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Coupling Models for One-Step Discrete Generation
Coupling Models enable single-step discrete sequence generation via learned couplings to Gaussian latents and outperform prior one-step baselines on text perplexity, biological FBD, and image FID metrics.