arxiv: 2604.18995 · v1 · submitted 2026-04-21 · 💻 cs.CL · cs.AI· cs.LG

Recognition: unknown

R²-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

Binfei Ji, Brucek Khailany, Kejing Xia, Nicolai Oswald, Pavlo Molchanov, Xinrui Zhong, Yingyan Lin, Yonggan Fu, Zhenbang Du

Pith reviewed 2026-05-10 02:52 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords diffusion large language modelsdecoding accelerationredundancy reductioninference optimizationsupervised fine-tuningparallel token generation

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The pith

R²-dLLM reduces diffusion LLM decoding steps by up to 75 percent by removing spatial and temporal redundancies during parallel token generation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Diffusion large language models predict many tokens at once but still require many decoding steps because the process repeats work on already stable predictions and on groups of tokens with similar uncertainties. The paper identifies spatial redundancy from confidence clusters and positional ambiguity, plus temporal redundancy from remasking tokens that no longer change. It counters these patterns with simple inference rules that combine nearby confidence scores and lock in finished tokens early, plus a fine-tuning step that trains the model to follow shorter, more efficient paths. If the reductions hold, dLLMs become far more practical for real applications by cutting latency while keeping output quality on par with standard methods.

Core claim

The paper establishes that a substantial share of dLLM decoding inefficiency stems from spatial redundancy caused by confidence clusters and positional ambiguity, together with temporal redundancy from repeatedly remasking predictions that have already stabilized. R²-dLLM counters this with training-free decoding rules that aggregate local confidence and token predictions and finalize temporally stable tokens, combined with a redundancy-aware supervised fine-tuning pipeline that aligns the model to efficient decoding trajectories, achieving up to 75 percent fewer decoding steps while preserving competitive generation quality across models and tasks.

What carries the argument

R²-dLLM framework of local confidence aggregation rules and stable-token finalization at inference time, plus redundancy-aware supervised fine-tuning to align models with shorter decoding paths.

If this is right

Existing dLLM models can be accelerated at inference time with no retraining using the aggregation and finalization rules.
Redundancy-aware fine-tuning produces models that naturally follow shorter decoding trajectories by design.
The step reductions and quality preservation hold across different diffusion LLM architectures and generation tasks.
Decoding latency drops substantially while standard quality metrics remain competitive with existing strategies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar redundancy patterns may exist in other non-autoregressive or diffusion-based sequence generators, opening a route to broader efficiency gains.
Lower step counts could allow dLLMs to compete on latency with autoregressive models in interactive settings such as chat or code completion.
The approach could be combined with other acceleration methods like caching or quantization for compounded speedups in deployed systems.

Load-bearing premise

That the observed patterns of spatial redundancy from confidence clusters and temporal redundancy from remasking stabilized predictions appear consistently enough across different dLLM models and tasks for the aggregation rules and fine-tuning to remove them without quality loss.

What would settle it

Apply R²-dLLM to a new dLLM architecture or task and check whether step count reduction falls below 50 percent or quality metrics such as task accuracy drop compared with baseline decoding.

Figures

Figures reproduced from arXiv: 2604.18995 by Binfei Ji, Brucek Khailany, Kejing Xia, Nicolai Oswald, Pavlo Molchanov, Xinrui Zhong, Yingyan Lin, Yonggan Fu, Zhenbang Du.

**Figure 1.** Figure 1: Benchmarking the accuracy versus the Number of Function Evaluations (NFE) trade-offs between our R 2 -dLLM and SOTA dLLM acceleration methods on the GSM8K dataset based on the LLaDA-Instruct-8B model. toRegressive (AR) paradigm (Brown et al., 2020; Ouyang et al., 2022). Unlike AR models, which are constrained by a sequential and token-by-token generation bottleneck, dLLMs leverage bidirectional attention … view at source ↗

**Figure 2.** Figure 2: An illustration of spatial redundancy. 4.1. Spatial Redundancy Unlike AR models that decode tokens in a fixed order, dLLMs generate tokens in a random order. While this enables parallel token prediction, it also introduces uncertainty over token positions. As a result, dLLMs must jointly determine both the token content and positions, which causes spatial redundancy during decoding. One common form of sp… view at source ↗

**Figure 3.** Figure 3: Overview of the proposed training-free redundancy reduction strategy during diffusion decoding. Token Cluster Aggregation. When the same token is predicted at multiple adjacent positions (e.g., ≥ 2 positions), we treat them as a token cluster. Given a cluster C = {i1, . . . , ik}, we select the position with the maximum confidence, i ∗ = arg max i∈C γi . (3) If γi ∗ > τs, the token is directly decoded at … view at source ↗

**Figure 4.** Figure 4: An illustration of temporal redundancy, where a “redundant step” denotes a token that matches the final output but is not finalized. dLLMs generate text through an iterative unmasking– remasking process. At each decoding step, the model predicts tokens at masked positions and only finalizes a subset of them based on the prediction confidence, while the remaining tokens are remasked and unmasked in later… view at source ↗

**Figure 5.** Figure 5: Redundancy-aware training dataset collection: For each prompt, candidate responses with correct answers and the lowest redundancy score Rtotal are selected. 5.1. Training Dataset Collection We construct a redundancy-aware training dataset by explicitly selecting decoding trajectories with minimal redundancy, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 6.** Figure 6: Training dynamics of LLaDA-Instruct-8B on GSM8K, showing loss, Rtotal, NFE, and accuracy over training iterations. 6.6. Training Dynamics of Redundancy-aware Supervised Fine-Tuning [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: The sensitivity of τs and τt. We conduct experiments to study the sensitivity of the spatial threshold τs and the temporal threshold τt [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Effect of Generation Length on Decoding Efficiency and Accuracy. A.3. Quantitative Analysis of Redundancy We further quantify how often each redundancy pattern appears during decoding. We conduct this analysis on LLaDA GSM8K and report the average number of events per response [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

read the original abstract

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding still suffers from high inference latency, which limits deployment. In this work, we observe that a substantial part of this inefficiency comes from recurring redundancy in the decoding process, including spatial redundancy caused by confidence clusters and positional ambiguity, and temporal redundancy caused by repeatedly remasking predictions that have already stabilized. Motivated by these patterns, we propose $R^2$-dLLM, a unified framework for reducing decoding redundancy from both inference and training perspectives. At inference time, we introduce training-free decoding rules that aggregate local confidence and token predictions, and finalize temporally stable tokens to avoid redundant decoding steps. We further propose a redundancy-aware supervised fine-tuning pipeline that aligns the model with efficient decoding trajectories and reduces reliance on manually tuned thresholds. Experiments demonstrate that $R^2$-dLLM consistently reduces the number of decoding steps by up to 75% compared to existing decoding strategies, while maintaining competitive generation quality across different models and tasks. These results validate that decoding redundancy is a central bottleneck in dLLMs, and that explicitly reducing it yields substantial practical efficiency gains.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes R²-dLLM, a framework for accelerating diffusion large language models (dLLMs) by reducing spatio-temporal redundancy during decoding. It identifies spatial redundancy arising from confidence clusters and positional ambiguity, and temporal redundancy from repeated remasking of stabilized predictions. The method introduces training-free decoding rules that aggregate local confidence and token predictions while finalizing temporally stable tokens, together with a redundancy-aware supervised fine-tuning (SFT) pipeline to align the model with efficient trajectories. The central empirical claim is that these changes reduce the number of decoding steps by up to 75% relative to existing strategies while preserving competitive generation quality across models and tasks.

Significance. If the reported speedups and quality preservation hold under broader scrutiny, the work could meaningfully advance the practicality of dLLMs by targeting a core inference bottleneck. The training-free aggregation rules and the integration of redundancy-aware SFT constitute clear strengths, as does the explicit framing of redundancy as an exploitable pattern rather than an incidental artifact. These elements, if shown to generalize, would provide a concrete path toward lower-latency parallel generation without requiring architectural overhauls.

major comments (2)

[Abstract / Experiments] Abstract and Experiments section: the headline claim of 'up to 75% step reduction' is presented without enumeration of the concrete baselines, model sizes, tasks, number of runs, error bars, or data-exclusion criteria. This omission directly affects verifiability of the central result and prevents assessment of whether the observed gains are robust or task-specific.
[Method / Experiments] The weakest assumption—that confidence clusters, positional ambiguity, and stabilized remasking predictions are reliably present and exploitable across dLLM architectures and task domains—is not supported by ablations on high- versus low-ambiguity tasks or by per-step quality trajectory comparisons. Without such evidence, the guarantee of 'no quality loss' under the aggregation rules plus SFT remains untested and load-bearing for the transferability claim.

minor comments (1)

[Method] Notation for the aggregation rules (local confidence, temporal stability thresholds) should be defined with explicit symbols and ranges in the main text rather than left to supplementary material.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of clarity and empirical robustness. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract / Experiments] Abstract and Experiments section: the headline claim of 'up to 75% step reduction' is presented without enumeration of the concrete baselines, model sizes, tasks, number of runs, error bars, or data-exclusion criteria. This omission directly affects verifiability of the central result and prevents assessment of whether the observed gains are robust or task-specific.

Authors: We agree that greater explicitness in the abstract and Experiments section would improve verifiability. The full Experiments section already contains tables reporting results on multiple models and tasks with comparisons to standard decoding strategies, but we will revise the abstract to briefly enumerate the primary baselines, model scales, and task categories. We will also add a dedicated paragraph in the Experiments section that details the number of runs, reports error bars or variance where applicable, and clarifies any data exclusion criteria. These changes will make the 75% reduction claim easier to assess without altering the underlying results. revision: yes
Referee: [Method / Experiments] The weakest assumption—that confidence clusters, positional ambiguity, and stabilized remasking predictions are reliably present and exploitable across dLLM architectures and task domains—is not supported by ablations on high- versus low-ambiguity tasks or by per-step quality trajectory comparisons. Without such evidence, the guarantee of 'no quality loss' under the aggregation rules plus SFT remains untested and load-bearing for the transferability claim.

Authors: We acknowledge that explicit ablations on ambiguity levels and per-step trajectories would provide stronger support for generalizability. Our reported results already cover multiple dLLM architectures and tasks that exhibit varying degrees of redundancy (as reflected in the range of observed speedups), and final quality metrics remain competitive. However, we did not include dedicated high-/low-ambiguity splits or step-wise quality curves. In the revision we will add (i) an analysis that proxies task ambiguity via metrics such as prediction entropy and cluster size, reporting corresponding speedups and quality, and (ii) per-step quality trajectory plots that compare our method against baselines to confirm quality preservation throughout decoding. These additions will directly test the load-bearing assumption. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical rules and SFT validated by experiment

full rationale

The paper's core contribution is an empirical framework: observations of spatial/temporal redundancy patterns motivate training-free aggregation rules for early token finalization plus a redundancy-aware SFT pipeline. No equations, first-principles derivations, or predictions are presented that reduce by construction to fitted parameters or self-referential definitions. Results are reported via direct experiments measuring step reduction and quality across models/tasks, with no load-bearing self-citations or uniqueness theorems invoked. The approach is self-contained as an engineering optimization whose validity rests on external benchmarks rather than internal redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Assessment limited to abstract; no explicit free parameters, invented entities, or non-standard axioms are described. The work rests on the domain assumption that dLLMs exhibit substantial recurring redundancy amenable to rule-based and training-based mitigation.

axioms (1)

domain assumption dLLMs exhibit spatial redundancy from confidence clusters and positional ambiguity plus temporal redundancy from remasking stabilized predictions
This observation is presented as the core motivation for the proposed framework.

pith-pipeline@v0.9.0 · 5558 in / 1283 out tokens · 48852 ms · 2026-05-10T02:52:24.300147+00:00 · methodology

discussion (0)

Reference graph

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