NAVIRA: Decoupled Stochastic Remasking for Masked Diffusion Language Models

• URL: http://arxiv.org/abs/2606.06031v1
• Date: Thu, 04 Jun 2026 11:24:47 GMT
• Title: NAVIRA: Decoupled Stochastic Remasking for Masked Diffusion Language Models
• Abstract summary: PRISM addresses this by learning token-level quality scores and remasking unreliable tokens.<n>We propose NAVIRA, an inference-time decoding policy that separates these two operations.<n>Temperature-controlled remasking reduces repeated correction of the same positions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked diffusion language models generate text by iteratively unmasking many tokens in parallel, but this speed comes with a correction problem: tokens generated in the same step are predicted from marginal distributions, and early local dependency errors can later contaminate the context. PRISM addresses this by learning token-level quality scores and remasking unreliable tokens, but its inference rule is coupled: the same forward pass both detects low-quality tokens and computes logits for their replacements, so the erroneous tokens still condition regeneration. We propose NAVIRA, an inference-time decoding policy that separates these two operations and samples remasking positions stochastically. A first forward pass scores tokens; selected tokens are masked; a second forward pass regenerates from the cleaned context. Temperature-controlled remasking reduces repeated correction of the same positions and balances fluency against diversity. In controlled experiments with a 170M masked diffusion language model, decoupling improves fluency, while scheduled stochastic remasking preserves entropy and achieves stronger LLM-judge scores under larger forward-pass budgets. These results show that remasking policy, not only the learned quality signal, is central to reliable masked-diffusion text generation.

Related papers

• Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models [59.51249894128724]
  Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration.<n>We propose ADAS, a training-free reranking rule for parallel masked diffusion decoding.
  arXiv  Detail & Related papers  (2026-06-09T13:17:27Z)
• MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training [53.777757218665386]
  MaskAlign is a token-subset representation alignment method that applies alignment to randomly sampled token subsets during training.<n>We show that MaskAlign reduces the dependence of representation alignment on the complete token set and encourages alignment behavior that is more stable under token-subset perturbations.
  arXiv  Detail & Related papers  (2026-06-07T19:11:22Z)
• SimSD: Simple Speculative Decoding in Diffusion Language Models [61.33773959352141]
  Diffusion large language models (dLLMs) offer faster inference through parallel or blockwise decoding.<n>Their masked language modeling formulation remains incompatible with standard token-level speculative decoding.<n>We propose a speculative decoding algorithm for dLLMs, named SimSD, that equips dLLMs with temporally valid token-level contexts.<n>Our method achieves up to 7.46x higher decoding throughput while maintaining and even improving average generation quality.
  arXiv  Detail & Related papers  (2026-06-01T17:46:46Z)
• Targeted Remasking: Replacing Token Editing with Token-to-Mask Refinement in Discrete Diffusion Language Models [0.6916773850242582]
  LLaDA2.1 introduced a Token-to-Token (T2T) editing mechanism that accelerates generation.<n>We propose Token-to-Mask (T2M) remasking, a training-free, drop-in replacement for T2T editing.<n>T2M resets suspected erroneous tokens back to the mask state, allowing the diffusion process to re-predict them under cleaner context.
  arXiv  Detail & Related papers  (2026-04-06T17:39:11Z)
• Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models [21.699371484195865]
  We propose DEMASK, a lightweight dependency predictor that attaches to the final hidden states of a dLLM.<n> Empirically, DEMASK achieves 1.7-2.2$times$ speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines.
  arXiv  Detail & Related papers  (2026-04-02T22:21:24Z)
• Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference [58.189320101488725]
  DLLMs promise fast non-autoregressive inference but suffer a severe quality-speed trade-off in parallel decoding.<n>We address this by integrating continuous representations into the discrete decoding process, as they preserve rich inter-position dependency.<n>We propose ReMix, a framework that introduces a novel Continuous Mixing State as an intermediate between the initial masked state and the final decoded token state.
  arXiv  Detail & Related papers  (2026-02-26T11:08:11Z)
• Where-to-Unmask: Ground-Truth-Guided Unmasking Order Learning for Masked Diffusion Language Models [17.18632315520133]
  Masked Diffusion Language Models generate text by iteratively filling masked tokens.<n>Gt-Margin yields an oracle unmasking order that prioritizes easier positions first under each partially masked state.<n>We train a supervised unmasking planner via learning-to-rank to imitate the oracle ordering from masked contexts.
  arXiv  Detail & Related papers  (2026-02-10T07:56:46Z)
• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• Learning Unmasking Policies for Diffusion Language Models [33.44995119635116]
  Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks.<n>One particularly successful variant is masked discrete diffusion, in which a buffer filled with special mask tokens is progressively replaced with tokens sampled from the model's vocabulary.<n>In this work, we propose to train sampling procedures using reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-09T20:44:33Z)
• Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Models [40.902681492117786]
  RemeDi is a mask-based DLM that predicts token distributions and per-token confidence scores at each step.<n>We train a remask-aware pipeline to train this ability, including supervised fine-tuning which teaches the model to detect and remask incorrect tokens.<n>Experiments show that RemeDi achieves the state-of-the-art results among open-source DLMs on multiple datasets.
  arXiv  Detail & Related papers  (2025-09-28T05:39:49Z)
• Blockwise SFT for Diffusion Language Models: Reconciling Bidirectional Attention and Autoregressive Decoding [60.06816407728172]
  Discrete diffusion language models have shown strong potential for text generation.<n>Standard supervised fine-tuning misaligns with semi-autoregressive inference.<n>We propose Blockwise SFT, which partitions responses into fixed-size blocks.
  arXiv  Detail & Related papers  (2025-08-27T02:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.