DELA introduces a plug‑in framework for diffusion language models that enables explicit draft‑level editing using four actions: Insert, Delete, Remask, and Stop. The system separates high‑level edit control from token‑level generation by employing a frozen diffusion language model and a two‑phase decoding process that first drafts append‑only content and then refines the full draft. Training the controller through a staged curriculum and rollout‑based preferences yields up to 5.5 percentage‑point improvements on mathematical reasoning and code generation tasks compared to direct diffusion decoding.
arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...
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.
arXiv:2607. 22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable.
arXiv:2604. 18738v3 Announce Type: replace Abstract: Diffusion language models (dLLMs) generate text through iterative denoising, filling multiple masked positions at each step.
The paper introduces Lookahead Editing (LED), a method that enhances masked diffusion language models (MDMs) by adding a rewritable proposal state. LED performs multiple lookahead updates at each decoding step to refine future predictions before committing tokens, thereby improving expressivity while retaining MDMs’ simple transition schedule. Fine‑tuned LED models (LED‑7B and LED‑8B) achieve significant performance gains on eight benchmarks and outperform larger models such as LLaDA2.1‑16B using only supervised fine‑tuning.
Draft Refinement Language Models (DRLMs) propose parallel revisions to a draft answer using a single denoiser that reconstructs every token from uniformly corrupted drafts, enabling both preservation and repair of tokens. The model evaluates each candidate draft by estimating a cavity energy from forward predictions, selecting revisions that lower this energy without a separate scoring model. Experiments on Countdown, Sudoku-Extreme, and Maze-Hard show DRLM outperforming autoregressive and discrete diffusion baselines, achieving 44.4% on Countdown-5 and 93.9% on Sudoku-Extreme with 6M parameters.
arXiv:2605. 26436v2 Announce Type: replace-cross Abstract: Discrete masked diffusion language models such as LLaDA generate text through iterative denoising, where mask tokens are progressively replaced with predicted tokens.
arXiv:2610.27379v1 Announce Type: cross Abstract: Block-diffusion speculative decoding accelerates large language model inference by drafting multiple tokens in parallel. However, one-step drafting i...
AutoIndexer is a generative model that trains causal models on chains of edits, enabling flexible post‑generation modifications such as token insertions, substitutions, and deletions. It introduces marker tokens for opening and closing edits and moves a cursor through the token sequence, mimicking a text editor. By perturbing ground‑truth labels with inverse edit operations during training, AutoIndexer becomes robust to erroneous context and can perform editing tasks like code correction while maintaining performance on standard LLM benchmarks.
arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders...
arXiv:2607. 01978v1 Announce Type: new Abstract: Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) with bounded overhead and minimal disruption to unrelated behaviors.
arXiv:2608.29748v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive language model inference by having a lightweight draft model propose multiple candidate tokens, which a...