arXiv:2606. 18856v1 Announce Type: cross Abstract: Sequence labelling, a core task of Natural Language Processing (NLP), consists in assigning each token of an input sentence a label.
arXiv:2606. 18856v3 Announce Type: replace-cross Abstract: We introduce Diffusion-MF, a discrete diffu- sion sequence labeller that places a linear-chain conditional random field (LCRF) inside the denoising loop.
arXiv:2608. 14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification.
arXiv:2602. 11133v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability long before the final denoising step.
arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
arXiv:2601. 12247v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches.
We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens.
The paper introduces a survival-guided length control method for diffusion language models (DLMs), framing length selection as a discrete-time survival problem over the end-of-sequence token. This training‑free, plug‑in length predictor can be added to any existing DLM and reduces unnecessary denoising steps. Experiments on reasoning and code‑generation benchmarks show up to a seven‑fold speedup in inference while maintaining task accuracy, and reveal that predicted lengths vary significantly even within the same dataset, affecting model performance.
The paper introduces Independent Token Sampling (ITS), a query‑efficient method for detecting memorized training data in diffusion large language models (dLLMs). ITS selects token sets with weak internal dependency by approximating cumulative conditional mutual information using an attention‑derived pairwise dependency proxy and promotes diversity across sampling rounds. Experiments show ITS outperforms existing baselines, improving AUC by 0.18 on the ArXiv dataset while remaining effective under limited query budgets.
arXiv:2506. 10292v2 Announce Type: replace-cross Abstract: Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data.
arXiv:2609.38795v1 Announce Type: new Abstract: Block drafters for speculative decoding are commonly trained on corpora written by external models, where a single off-policy token invalidates supervi...
The paper introduces Iterative MBR Distillation for Error Span Detection (ESD) in machine translation, a self‑evolution framework that replaces human annotations with pseudo‑labels generated by a large language model. By iteratively applying Minimum Bayes Risk decoding, the method produces high‑quality error spans without costly human effort. Experiments on WMT Metrics Shared Task datasets show that models trained solely on these pseudo‑labels outperform both unadapted baselines and supervised models trained on human data at system and span levels, while keeping sentence‑level performance competitive.