arXiv:2601. 22947v2 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive language models.
arXiv:2606. 16847v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality.
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 Corrective Diffusion Language Models (CDLMs), which enhance masked diffusion language models by explicitly supervising visible corrupted tokens to enable confidence-guided refinement of incorrect tokens. Experiments show that this correction-oriented post‑training improves iterative refinement and parallel decoding across models ranging from 0.5B to 8B, outperforming standard masked‑diffusion fine‑tuning on benchmarks such as Code Revision, HumanEval(+), and MATH. The authors also provide a new Code Revision Benchmark and release code and data for reproducibility.
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:2607. 20467v1 Announce Type: new Abstract: While parallel decoding is central to the efficiency of Diffusion Large Language Models (dLLMs), current strategies are often hindered by overly conservative confidence thresholds.
The paper introduces DARTS, a method for tuning decoder representations during model merging. It addresses representation bias in autoregressive decoders by using an entropy‑weighted L1 loss and a per‑position additive bias to correct errors that accumulate across token positions. Experiments on code generation, mathematical reasoning, and instruction following with Llama‑2‑7B show that DARTS improves performance over standard surgery while adding only 0.1% extra parameters.
arXiv:2609. 04010v1 Announce Type: new Abstract: Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation.
arXiv:2608. 13925v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) accelerate language generation by predicting multiple masks in a single forward pass.
The study compares continuous diffusion language models (CDLMs) and speculative drafters by training them from scratch on unconditional OpenWebText generation at lengths 16, 128, and 1024. Using LangFlow for CDLMs and adapted DSpark Markov and recurrent-head architectures for standalone generation, the authors evaluate generation perplexity (GenPPL) and MAUVE similarity. Results show LangFlow outperforms both DSpark variants at longer lengths (128 and 1024), while DSpark-RNN leads at length 16; recurrent memory and teacher probability matching improve metrics but the longer‑sequence gap remains, highlighting iterative full‑sequence refinement as a key advantage.
SimLens is a training‑free decoder that improves early‑layer predictions in large language models by keeping only the start token and a candidate answer token and performing a lightweight continuation through the remaining layers. It outperforms direct linear readouts, yielding higher accuracy on tasks such as ARC, BoolQ, and HeadQA with LLaMA‑7B and Vicuna‑7B. The method is extended to Linear SimLens for confidence estimation and combined into SimExit, a hybrid early‑exit mechanism that achieves significant speedups while maintaining accuracy.
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.