arXiv:2605. 00161v2 Announce Type: replace Abstract: Diffusion language models (DLMs) are an attractive alternative to autoregressive models because they promise sublinear-time, parallel generation, yet practical gains remain elusive as high-quality samples still demand hundreds of refinement steps.
The paper examines how Denoising Diffusion Probabilistic Models (DDPMs) perform on globally constrained discrete tasks such as Sudoku and N-queens. It shows that standard diffusion sampling, which keeps updates close to the noisy state, often preserves early mistakes, whereas sampling directly from the model’s clean predictions dramatically improves validity (e.g., Sudoku from 31% to 95%). The authors further introduce self‑correction training, exposing the model to its own predictions to reduce inference errors, which enhances the performance of standard samplers across tasks.
The article "Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction" presents a unified primer on diffusion models that applies to both continuous Euclidean data and discrete categorical structures. It develops discrete-time forward noising via Markov kernels and learned reverse dynamics, and connects these to continuous-time limits such as stochastic differential equations in ρ^d and continuous-time Markov chains on finite alphabets, deriving the corresponding Fokker–Planck and master equations. The work also shows how different forward corruption choices—Gaussian processes for continuous spaces and structured categorical transition kernels for discrete spaces—affect reverse dynamics and the evidence lower bound used in training, offering a layered exposition for newcomers, practitioners, and experts alike.
arXiv:2610.02193v1 Announce Type: cross Abstract: Discrete diffusion language models offer a compelling alternative to autoregressive generation for tasks demanding bidirectional reasoning and global...
arXiv:2607. 24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling.
The paper introduces Less Uniform Diffusion (LUDI), a framework that improves uniform diffusion language models by using a less uniform loss and per-token time embeddings to guide reverse transitions and enable confidence-based few-step sampling. Experiments demonstrate that LUDI provides cleaner supervision, enhances few-step generation, and scales to a 7B model (LUDI-7B) that achieves a 3-token-per-step speedup over autoregressive decoding while matching masked diffusion baselines. The work suggests that UDLMs still have untapped potential for complex generation tasks.
arXiv:2606. 28301v1 Announce Type: new Abstract: Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards.
arXiv:2604. 26985v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process.
arXiv:2609.40235v1 Announce Type: cross Abstract: Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (...
arXiv:2609.37533v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically...
arXiv:2608. 10384v1 Announce Type: new Abstract: This paper studies inverse sampling for L\'evy-driven generative models from the perspective of Markov generators.
Forward-Free Diffusion Language Models with BPTT-Free Looped Refinement (FReDA) removes the need for a hand‑designed forward process in diffusion language modeling by treating model‑generated drafts as implicit intermediate states and refining them recursively. The approach detaches earlier refinement passes, backpropagating only through the final pass, and supports both self‑refinement and Best‑of‑N candidate selection. In sub‑8B experiments, FReDA‑4B surpasses larger diffusion baselines on reasoning and coding tasks, achieving up to 15% absolute gains and a 1.5‑1.8× speedup while scaling well with additional refinement steps.