Reversible Diffusion Decoding for Diffusion Language Models

• URL: http://arxiv.org/abs/2602.00150v1
• Date: Thu, 29 Jan 2026 12:52:33 GMT
• Title: Reversible Diffusion Decoding for Diffusion Language Models
• Authors: Xinyun Wang, Min Zhang, Sen Cui, Zhikang Chen, Bo Jiang, Kun Kuang, Mingbao Lin,
• Abstract summary: Reversible Diffusion Decoding (RDD) is a decoding framework that introduces reversibility into block-wise diffusion generation.<n>RDD detects stagnation as a state-dependent failure of the reverse process and enables efficient backtracking to earlier blocks.<n> Experiments show that RDD improves generation robustness and quality over baselines with minimal computational overhead.
• Score: 69.10149777322108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion language models enable parallel token generation through block-wise decoding, but their irreversible commitments can lead to stagnation, where the reverse diffusion process fails to make further progress under a suboptimal context.We propose Reversible Diffusion Decoding (RDD), a decoding framework that introduces reversibility into block-wise diffusion generation. RDD detects stagnation as a state-dependent failure of the reverse process and enables efficient backtracking to earlier blocks without recomputation via cached model states. To avoid repeated failure trajectories, RDD applies confidence-guided re-masking to selectively reinitialize uncertain tokens while preserving reliable context.This reversible formulation allows decoding to recover from early commitment errors while maintaining the parallel efficiency of diffusion-based generation. Experiments show that RDD improves generation robustness and quality over baselines with minimal computational overhead.

Related papers

• 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)
• Causal Autoregressive Diffusion Language Model [70.7353007255797]
  CARD reformulates the diffusion process within a strictly causal attention mask, enabling dense, per-token supervision in a single forward pass.<n>Our results demonstrate that CARD achieves ARM-level data efficiency while unlocking the latency benefits of parallel generation.
  arXiv  Detail & Related papers  (2026-01-29T17:38:29Z)
• Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Models [1.3535770763481902]
  Foundation models, despite their robust zero-shot capabilities, remain vulnerable to spurious correlations and 'Clever Hans' strategies.<n>We propose Visual Disentangled Diffusion Autoencoders (DiDAE), a novel framework integrating frozen foundation models with disentangled dictionary learning.<n>DiDAE first edits foundation model embeddings in interpretable disentangled directions of the disentangled dictionary and then decodes them via a diffusion autoencoder.
  arXiv  Detail & Related papers  (2026-01-29T15:25:37Z)
• Deferred Commitment Decoding for Diffusion Language Models with Confidence-Aware Sliding Windows [33.361153168706444]
  We propose Deferred Commitment Decoding (DCD) as a training-free decoding strategy.<n>DCD maintains a confidence-aware sliding window over masked tokens, resolving low-uncertainty tokens early while deferring high-uncertainty tokens until sufficient contextual evidence becomes available.<n>Experiments show that DCD improves generation accuracy by 1.39% with comparable time on average compared to fixed block-based diffusion methods, with the most significant improvement reaching 9.0%.
  arXiv  Detail & Related papers  (2026-01-05T12:57:33Z)
• From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs [58.640039233470766]
  We show that principled AR-to-block-diffusion adaptation is an effective and compute-efficient alternative to training DLMs from scratch.<n> NBDiff-7B (Base and Instruct) could inherit the long-context modeling and reasoning capabilities, and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-07T10:28:21Z)
• WavefrontDiffusion: Dynamic Decoding Schedule or Improved Reasoning [39.57157800995735]
  We present WavefrontDiffusion, a dynamic decoding approach that expands a wavefront of active tokens outward from finalized positions.<n>Across four benchmarks in reasoning and code generation, WavefrontDiffusion achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-11-22T07:33:00Z)
• Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding [51.711605076319216]
  Diffusion-based large language models (Diffusion LLMs) have shown promise for non-autoregressive text generation with parallel decoding capabilities.<n>We introduce a novel block-wise approximate KV Cache mechanism tailored for bidirectional diffusion models, enabling cache reuse with negligible performance drop.<n>We propose a confidence-aware parallel decoding strategy that selectively decodes tokens exceeding a confidence threshold, mitigating dependency violations and maintaining generation quality.
  arXiv  Detail & Related papers  (2025-05-28T17:39:15Z)
• Look-back Decoding for Open-Ended Text Generation [62.53302138266465]
  We propose Look-back, an improved decoding algorithm that tracks the distribution distance between current and historical decoding steps. Look-back can automatically predict potential repetitive phrase and topic drift, and remove tokens that may cause the failure modes. We perform decoding experiments on document continuation and story generation, and demonstrate that Look-back is able to generate more fluent and coherent text.
  arXiv  Detail & Related papers  (2023-05-22T20:42:37Z)

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.