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Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
diffusion language modelscompute efficient samplingskipping computeadaptive attention
Abstract:

Masked Diffusion Language Models generate sequences via iterative sampling that progressively unmasks tokens. However, they still recompute the attention and feed-forward blocks for every token position at every step---even when many unmasked tokens are essentially fixed, resulting in substantial waste in compute. We propose \textbf{\textsc{SureLock}}: when the posterior at an unmasked position has stabilized across steps (our \emph{sure} condition), we \emph{lock} that position---thereafter skipping its query projection and feed-forward sublayers---while caching its attention keys and values so other positions can continue to attend to it. This reduces the dominant per-iteration computational cost from O(N2d)O(N^2d) to O(MNd)O(MNd) where NN is the sequence length, MM is the number of unlocked token positions, and dd is the model dimension. In practice, MM decreases as the iteration progresses, yielding substantial savings. On LLaDA-8B, SureLock reduces algorithmic FLOPs by 30--50% relative to the same sampler without locking, while maintaining comparable generation quality. We also provide a theoretical analysis to justify the design rationale of SureLock: monitoring only the local KL at the lock step suffices to bound the deviation in final token probabilities.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces SureLock, a method that locks converged token positions during masked diffusion decoding to skip redundant computation. It resides in the 'Computational Reuse and Caching Mechanisms' leaf, which contains only three papers total, including this work. This leaf represents a relatively sparse research direction within the broader taxonomy of masked diffusion efficiency techniques, suggesting that explicit computational reuse strategies based on token convergence are less explored compared to alternative approaches like training-based distillation or architectural modifications.

The taxonomy reveals neighboring branches focused on inference-time sampling strategies (e.g., heuristic-based unmasking policies with six papers, search-based methods with three papers) and architectural modifications (e.g., block-based decoding, partial masking schemes). SureLock diverges from these by neither altering the unmasking schedule nor modifying model architecture; instead, it exploits posterior stability to reduce per-iteration cost. The closest conceptual neighbors are KV caching methods within the same leaf, which also reuse intermediate representations but do not explicitly halt computation for converged positions based on distributional criteria.

Among the three contributions analyzed, the literature search examined eighteen candidates total. The core SureLock locking mechanism was evaluated against eight candidates with zero refutations found. Similarly, the local KL divergence criterion for lock decisions faced eight candidates with no clear prior work identified. The theoretical bound linking local KL to terminal error was assessed against two candidates, again with no refutations. These statistics reflect a limited search scope (top-K semantic matches plus citations), not an exhaustive survey, but suggest that within the examined set, no prior work directly anticipates the specific combination of convergence detection and selective computation skipping proposed here.

Given the sparse population of the 'Computational Reuse and Caching Mechanisms' leaf and the absence of refutations among eighteen examined candidates, the work appears to occupy a relatively underexplored niche. However, the limited search scope means that closely related techniques in adjacent branches (e.g., adaptive sampling, KV caching variants) may exist but were not surfaced. The analysis covers top-ranked semantic matches and does not claim exhaustive coverage of all possible prior art in computational reuse for diffusion models.

Taxonomy

Core-task Taxonomy Papers
45
3
Claimed Contributions
18
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Reducing computational cost in masked diffusion language model decoding. The field addresses the challenge that masked diffusion models, while offering flexible generation capabilities, require many iterative denoising steps that can be prohibitively expensive. The taxonomy reveals a diverse landscape of solutions organized into several major branches. Inference-Time Sampling and Unmasking Strategies explore adaptive scheduling and token selection policies, such as Lookahead Unmasking[6] and Dilated Scheduling[5], which aim to unmask tokens more intelligently during generation. Training-Based Efficiency Improvements focus on learning better policies or distilling models to reduce steps, exemplified by Learning Unmasking Policies[4]. Architectural and Representation Modifications investigate structural changes to the model itself, while Computational Reuse and Caching Mechanisms seek to avoid redundant computation by storing and reusing intermediate results, as seen in KV Caching Acceleration[22] and Know Before Decoding[20]. Additional branches cover theoretical foundations, scaling studies, domain-specific adaptations, and auxiliary techniques like uncertainty quantification and reasoning enhancements. Within this landscape, a particularly active line of work centers on computational reuse strategies that exploit the iterative nature of diffusion decoding. Stopping Converged Tokens[0] sits squarely in this branch, proposing to halt computation for tokens that have already stabilized, thereby avoiding wasteful forward passes. This approach contrasts with caching methods like KV Caching Acceleration[22], which store key-value pairs to accelerate attention, and Know Before Decoding[20], which precomputes information to guide the decoding process. While these neighboring works focus on reusing or precomputing representations, Stopping Converged Tokens[0] emphasizes dynamic early stopping based on convergence detection. The broader tension across branches involves balancing generation quality with speed: some methods sacrifice a degree of flexibility for fewer steps, while others maintain full iterative refinement but seek smarter ways to skip redundant computation. Open questions remain about how to best detect convergence, generalize caching across diverse tasks, and integrate these efficiency gains with emerging training-based distillation techniques.

Claimed Contributions

SureLock method for locking converged tokens in masked diffusion decoding

The authors introduce SureLock, a method that permanently stops computation for token positions whose posteriors have stabilized during iterative masked diffusion sampling. Once locked, these positions skip query projection and FFN sublayers while their cached K/V vectors remain available for other tokens to attend to, reducing per-step computational cost from O(N²d) to O(MNd) where M decreases over time.

8 retrieved papers
Local KL divergence criterion for determining when to lock token positions

The authors propose using step-wise KL divergence of token posteriors as the primary criterion for deciding when to lock a position. When the KL divergence between consecutive steps falls below a threshold epsilon, the position is locked, optionally combined with a confidence gate that prefers tokens with peaked posteriors.

8 retrieved papers
Theoretical bound linking local KL at lock time to terminal log-probability error

The authors derive a closed-form theoretical bound (Theorem 1) that connects the per-step KL divergence at the time of locking to the error in terminal token log-probabilities. This provides design justification for using local KL as the locking signal, showing that enforcing a KL threshold epsilon bounds the terminal error by delta equals C_tail times square root of epsilon.

2 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SureLock method for locking converged tokens in masked diffusion decoding

The authors introduce SureLock, a method that permanently stops computation for token positions whose posteriors have stabilized during iterative masked diffusion sampling. Once locked, these positions skip query projection and FFN sublayers while their cached K/V vectors remain available for other tokens to attend to, reducing per-step computational cost from O(N²d) to O(MNd) where M decreases over time.

Contribution

Local KL divergence criterion for determining when to lock token positions

The authors propose using step-wise KL divergence of token posteriors as the primary criterion for deciding when to lock a position. When the KL divergence between consecutive steps falls below a threshold epsilon, the position is locked, optionally combined with a confidence gate that prefers tokens with peaked posteriors.

Contribution

Theoretical bound linking local KL at lock time to terminal log-probability error

The authors derive a closed-form theoretical bound (Theorem 1) that connects the per-step KL divergence at the time of locking to the error in terminal token log-probabilities. This provides design justification for using local KL as the locking signal, showing that enforcing a KL threshold epsilon bounds the terminal error by delta equals C_tail times square root of epsilon.

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