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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Diffusion language mod- els can perform many tasks with scaling and instruction-finetuning
13 Pith papers cite this work. Polarity classification is still indexing.
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Graph random walks provide a verifiable sandbox for diagnosing parallel samplers in masked diffusion models, showing performance depends on graph structure and introducing a new exact bisection sampler.
RSPO interprets reward advantages as targets for relative log-ratios in dLLMs, calibrating noisy estimates to stabilize RLVR training and achieve strong gains on planning tasks with competitive math reasoning performance.
Pretrained language models are used as energy functions for Glauber dynamics in discrete text diffusion, improving generation quality over prior diffusion LMs and matching autoregressive models on benchmarks and reasoning tasks.
Fast-dLLM adds reusable KV cache blocks and selective parallel decoding to diffusion LLMs, closing most of the speed gap with autoregressive models without retraining.
First dedicated survey organizing diffusion and flow matching models for tabular data synthesis, imputation, anomaly detection, and related tasks, covering literature from 2015 to 2026 and highlighting open problems.
DPLM-Evo introduces an evolutionary discrete diffusion framework with explicit edit prediction and contextual noising that claims SOTA single-sequence mutation effect prediction on ProteinGym while supporting variable-length evolution simulation.
In diffusion language models, coarse linguistic labels stabilize earlier than exact token identity, uncertainty tracks correctness, and mid-trajectory states are most sensitive to perturbations.
Dream 7B is a 7B diffusion LLM that refines sequences in parallel via denoising and outperforms prior diffusion models on general, mathematical, and coding benchmarks with added flexibility in generation order and quality-speed tradeoffs.
LLaDA-V is a diffusion-based multimodal large language model that reaches competitive or state-of-the-art results on visual instruction tasks while using a non-autoregressive architecture.
Adapting autoregressive models via continual pre-training yields diffusion language models from 127M to 7B parameters that outperform prior diffusion models and compete with their autoregressive counterparts on language, reasoning, and commonsense benchmarks.
In the oracle continuous-time setting, stochastic interpolation models recover training samples exactly, with deviations controlled by discretization and estimation errors, leading to theoretical definitions of overfitting and underfitting.
BA-Att introduces pre-downsampled block selection with norm-sorting and diagonal covariance correction to approximate sparse attention, yielding up to 6.95x speedup at 50% sparsity across language, multimodal, and video models.
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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Understanding Parallel Samplers in Masked Diffusion via Random Walks on Graphs
Graph random walks provide a verifiable sandbox for diagnosing parallel samplers in masked diffusion models, showing performance depends on graph structure and introducing a new exact bisection sampler.
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Relative Score Policy Optimization for Diffusion Language Models
RSPO interprets reward advantages as targets for relative log-ratios in dLLMs, calibrating noisy estimates to stabilize RLVR training and achieve strong gains on planning tasks with competitive math reasoning performance.
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Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion
Pretrained language models are used as energy functions for Glauber dynamics in discrete text diffusion, improving generation quality over prior diffusion LMs and matching autoregressive models on benchmarks and reasoning tasks.
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Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding
Fast-dLLM adds reusable KV cache blocks and selective parallel decoding to diffusion LLMs, closing most of the speed gap with autoregressive models without retraining.
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Diffusion and Flow Matching Models for Tabular Data: A Survey
First dedicated survey organizing diffusion and flow matching models for tabular data synthesis, imputation, anomaly detection, and related tasks, covering literature from 2015 to 2026 and highlighting open problems.
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Towards A Generative Protein Evolution Machine with DPLM-Evo
DPLM-Evo introduces an evolutionary discrete diffusion framework with explicit edit prediction and contextual noising that claims SOTA single-sequence mutation effect prediction on ProteinGym while supporting variable-length evolution simulation.
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Measuring Temporal Linguistic Emergence in Diffusion Language Models
In diffusion language models, coarse linguistic labels stabilize earlier than exact token identity, uncertainty tracks correctness, and mid-trajectory states are most sensitive to perturbations.
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Dream 7B: Diffusion Large Language Models
Dream 7B is a 7B diffusion LLM that refines sequences in parallel via denoising and outperforms prior diffusion models on general, mathematical, and coding benchmarks with added flexibility in generation order and quality-speed tradeoffs.
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LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning
LLaDA-V is a diffusion-based multimodal large language model that reaches competitive or state-of-the-art results on visual instruction tasks while using a non-autoregressive architecture.
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Scaling Diffusion Language Models via Adaptation from Autoregressive Models
Adapting autoregressive models via continual pre-training yields diffusion language models from 127M to 7B parameters that outperform prior diffusion models and compete with their autoregressive counterparts on language, reasoning, and commonsense benchmarks.
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A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models
In the oracle continuous-time setting, stochastic interpolation models recover training samples exactly, with deviations controlled by discretization and estimation errors, leading to theoretical definitions of overfitting and underfitting.
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Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention
BA-Att introduces pre-downsampled block selection with norm-sorting and diagonal covariance correction to approximate sparse attention, yielding up to 6.95x speedup at 50% sparsity across language, multimodal, and video models.