Beyond Fixed: Training-Free Variable-Length Denoising for Diffusion Large Language Models
Authors: Jinsong Li
Xiaoyi Dong
Yuhang Zang
Yuhang Cao
Jiaqi Wang
Dahua Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Diffusion Language Model
Training-Free
Primary area: foundation or frontier models, including LLMs

Abstract: Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by a critical architectural constraint: the need for a statically predefined generation length.  This static length allocation leads to a problematic trade-off: insufficient lengths cripple performance on complex tasks, while excessive lengths incur significant computational overhead and sometimes result in performance degradation.  While the inference framework is rigid, we observe that the model itself possesses internal signals that correlate with the optimal response length for a given task.  To bridge this gap, we leverage these latent signals and introduce DAEDAL, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models.  DAEDAL operates in two phases: 1) Before the denoising process, DAEDAL starts from a short initial length and iteratively expands it to a coarse task-appropriate length, guided by a sequence completion metric.  2) During the denoising process, DAEDAL dynamically intervenes by pinpointing and expanding insufficient generation regions through mask token insertion, ensuring the final output is fully developed. Extensive experiments on DLLMs demonstrate that DAEDAL achieves performance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio. By resolving the static length constraint, DAEDAL unlocks new potential for DLLMs, bridging a critical gap with their Autoregressive counterparts and paving the way for more efficient and capable generation.

Published as a conference paper at ICLR 2026
BEYOND FIXED:
TRAINING-FRES VARIABLE-LENGTH DENOISING FOR DIFFUSION LARGE LANGUAGE MODELS
Jinsong Li Xiaoyi Dongl.2f Yuhang Zang² Yuhang Cao² Jiaqi Wang2.tt
Dahua Lin
The Chinese University of Hong Kong 2 Shanghai Al Laboratory
3 CPII under InnoHK Shanghai Innovation Institute
1 110240ie cuhk.edu .hk Github: https: /tt//gittt //ggithuubb wtt.hhu....ooiaieetteeeeea Ltt.://///-
ABSTRACT
Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by critical architectural constraint: the need for a statically predefined generation length. This static length allocation leads to a problematic trade-off:: insufficient lengths cripple performance complex tasks, while ex- cessive lengths incur significant computational overhead and sometimes result performance degradation. While the inference framework is rigid, observe that the model itself possesses internal signals that correlate with the optimal response length for given task. To bridge this gap, leverage these latent signals and introduce DAEDAL, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models. DAEDAL op- erates in two phases: 1) Before the denoising process, DAEDAL starts from a short initial length and iteratively expands
coarse tapk-appropriate length, guided by a sequence completion metric. 2) During the denoising process, DAEDAL dynamically intervenes by pinpointing and expanding insufficient generation re- gions through mask token insertion, ensuring the final output is fully developed. Extensive experiments on DILLMs demonstrate that DAEDAL achieves perfor- mance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio. By resolving the static length constraint, DAEDAL unlocks new potential for DLLMs, bridging a critical gap with their Autoregressive counterparts and paving the way for more efficient and capable generation.
INTRODUCTION
Diffusion Large Language Models (DLLMs) (Nie et al., 2025; DeepMind, 2025; Inception Labs et al., 2025) have recently emerged as a promising paradigm for Large Language Models (LLMs) (Yang et al., 2025; Achiam et al., 2023), garnering significant attention from both academia and industry. Unlike the conventional autoregressive (AR) framework, which generates text sequentially via next-token prediction, DLLMs operate through multi-step iterative denoising process. By leveraging bidirectional attention to refine an initially masked sequence into a coherent output, offers several distinct advantages (Nie et al., 2024), including the ability utilize global context for tasks that require holistic planning. a flexible tradc-off between the number of inference steps and the quality of the generated sample. Given these unique properties, DLLMs have become compelling research direction that offers an alternative to the autoregressive paradigm.
Despite their promising potential, the inference of DLLMs suffers from fundamental limitation rooted in their denoising paradigm: the denoise starts from a fully-masked sequence of fixed length,
TTiis work is done during an internship in Shanghai AT Laboratory. Corresponding author

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Published as a conference paper at ICLR 2026
45
50
Performance
8D
40
40
70
35
65
GSMBK
MATH500
30
MBPP
30
HUMANEVAL
30
86
25
50
Initial Length
25
Initial Length
Initial Length
Initial Length
15
64
1024
64 ... 512
1924
2048
512
1024
2048
-12
1024
2045
Fixed Length Denoising (Boseline)
DAEDAL
(a)
1310
The Best Best--erforming Baseline (83.8 6SM8K)
Number of Quastions
50
40
DAEDAL (85,8 on 65MBK)
30
20
Inihial Length
Predefined Length Baseline
10
DAEDAL
128
512
Length of Model's Responses
(b)
Figure 1: Overview of DAEDAL's effectiveness on LLaDA-Instruct-SB. (a) DAEDAL uses uni- fied and short initial length, consistently surpassing the baseline, which needs its length meticulously tuned for each benchmark to achieve peak performance. (b) DAEDAL dynamically adjusts length and adaptively expands on a per-problem basis, resulting in a varied distribution of response lengths. In contrast, the baseline is constrained to fixed length for all problems.
hence the final output length is also statically predefined. This static setup leads to flawed inference compared to their autoregressive counterpart: AR LLMs flexibly adjust the output length based on the given task, while DLLMs must adapt the task output based on the given length.
The requirement of manually pre-defined length leads to severe dilemma. An overly short length hinders the model from solving complex problems due to an insufficient token length. Conversely, universally adopting a long generation length introduces new set of issues. First, it incurs large computational overhead due to the quadratic complexity of bidirectional attention. Second, as shown in Figure (a), we observe that excessively long initial lengths may degrade model performance.
This rigid. pre-defined length constraint not only creates the dilemma at the outset of inference, but more fundamentally, it cripples the model's ability to adapt dynamically. For instance, DLLMs lack the critical test-time scaling capability (Muennighoff et al., 2025) of AR models, which can extend their output self-correct (c.g.. "Wait, let me rethink...') "" (Shah al., 2025; Wei al., 2022). This problem is exacerbated by the non-sequential generation nature of DLLMs. A model might generate the beginning and end of sequence first, only to find the allocated space for intermediate reasoning insufficient, leading directly to incomplete logic and degraded performance.
Fortunately, find the solution lies within the DLLM's intrinsic capabilities its planning ability. In each denoise step, the model plans the final output by predicting all the mask tokens with different confidence. We discovered that the model's prediction confidence signal indicating whether the generation space is sufficient. For example, as shown in Figure 2, in the first denoising step (t=1), the model confidently predicts more End-of -f--enneeeeeeeeeeeee tokens (EOS) from the fully masked sequence when the length is sufficient for the given task, while less confident in predicting EOS when the length insufficient.
as powerful, general-purpose
This core insight paves the way for variable-length denoising strategies, and we present DAEDAL, novel training-fre free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion I Large Language Models. DAEDAL operates in two phases: 1) Initial Length Adjustment. Before the denoising process, DAEDAL starts from short initial length and iteratively expands to coarse task-appropriate length. The expansion is guided by sequence completion metric, which calculated by the predicted confidence EOS tokens within a fixed window. 2) Iterative Mask Insertion. During the denoising process, DAEDAL dynamically expands the sequence to develop better output. The expansion is realized by inserting mask tokens into insufficient regions, where the model struggles to plan and the corresponding prediction confidence is quite low.
With DAEDAL, DLLMs no longer require manually tuned, task-specific generation lengths. Instead, they can start from short, unified initial length and dynamically expand needed. Experiments
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0,10
0.05
(a) GSM8K
0.00
0005
(b) MATH500
Figure 2: Visualization of the DLLM'S awareness of length sufficiency. The heatmaps show the difference in average EOS token confidence at the sequence terminus, measured after the first prediction on a fully masked 128-token input. This difference is the result of subtracting the average confidence congth-insufficient problems (those answered correctly only with a much longer sequence) from that on eength-sufficient problems (those answered correctly under 128 tokens). The experiment is conducted with L.aDA-IInttucct The predominantly green color (difference 0) indicates that EOS confidence is higher for length- -rutthssfffiient problems, validating our core insight.
demonstrate that DAEDAL not only allocates appropriate computational resources for diverse tasks, shown in Figure 1 (b), but also achieves performance that is comparable, and in some cases superior, to the peak performance of meticulously tuned fixed-length baselines. Our method thus achieves strong performance while significantly improving computational efficiency.
2
METHODS
2.1 OVERVIEW OF DIFFUSION LARGE LANGUAGE MODELS
Training. The training of a DLLM aims define and learn model distribution pe (zo) that approximates the true data distribution. This is achieved through forward and reverse probabilistic process (Austin al., 2021 la; Ou et al., 2024; Shi et al., 2024). The forward process defines fixed data noising mechanism indexed by continuous time variable t E [0, 1]. It progressively masks an original sequence xo until fully masked at 1. For any given f, a noised version 2, is generated by independently replacing each token in xo with a [MASK] token with probability t, while keeping unchanged with probability model, parameterized by 8, is trained to reverse the forward process by learning predict the original sequence zo from its noised version vr. This is achieved by optimizing the model to minimize cross-entropy loss computed only the masked token positions. This objective is principled corresponds to maximizing the Evidence Lower Bound (ELBO) on the data't 's log-likelin likkliihoodd, thereby pushing the model distribution py to approximate the true data distribution.
t. The reverse process is where learning occurs. A Transformer
During inference, LLaDA employs a multi-step iterative denoising process to generate text. As illustrated in Figure (a), this process begins with sequence length composed entirely of [MASK] tokens, where L is a predefined hyperparameter. In each denoising step f, the model takes the current sequence X, as input and predicts the tokens for all masked positions, yielding an estimate &n of the complete, clean sequence. Subsequently. "remasking" strategy is applied to determine which tokens are finalized and which are re-masked for the next denoising step, f. - LLaDA demonstrates the effectiveness of a fow-confidencc remasking" strategy (Nie al., 2025; Chang et al., 2022), where tokens predicted with the highest confidence are kept, while those with low confidence are re-masked for further refinement in subsequent steps. This iterative process continues for a fixed number of steps until the final text sequence is generated. This standard inference pipeline exposes problem: the generation length L must be statically specified before inference begins, preventing it from adapting to the actual requirements of the task.
Inference.
Further related works are provided in Appendix C.
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Prompt Token
MASK Token Predefined generation length
Predicted token (1 iteration)
Denoised tokens
The denoising order
! Model predicts tokens
positions.
2 Retain selected tokens and remask others.
① and
Repeat and
? until MASK tokens left.
Fixed-Length Denoising (Baseline)
Expanded MASK token
"Expansion point" token
Predicted taken (1st and 2nd iteration)
Initial length
Initial Length Adjustment
i Model predicts tokens positions.
Check EOS confidence, determine length is insufficient, and expand.
Iteratively check and expand until the length deemed suf ficientent
Iterative Mask Insertion
Model predicts the tokens for all MASK positions.
Remask and identify patential "expansion points" (if any).
Replace
with multiple MASK tokens to expond.
4
Repeat ( until MASK tokens left.
(b) DAEDAL
Figure 3: Inference process of Length Denoising (Baseline) and DAEDAL. (a) The stan- dard inference process for current DLLMs, which performs iterative denoising on a sequence of predefined, static length. (b) Our proposed two-stage inference process, which first employs Initial Length Adjustment to determine appropriate generation length before denoising, followed by Iterative Mask Insertion to expand the sequence on-demand during the denoising process.
2.2 DAEDAL
To address the static length limitation of standard DLLM inference, we introduce DAEDAL, traening-free, two-stage strategy. This approach allows the model to allocate appropriate sequence length for each task and to insert additional space for reasoning where needed. A detailed algorithmic description of this process is provided in Algorithm (Appendix D)).
2.2.1 INITIAL LENGTH ADJUSTMENT
The first stage of DAEDAL is Initial Length Adjustment. The core insight of it that the model's confidence generating an End-of-See End-of-Sequence of-Sequence (EOS) token the end of the sequence can be inter- preted as an internal signal of whether the current token length is sufficient. To validate this insight, we visualize the model's 's behavior Figure 2. Specifically, we measure the average EOS confidence at the sequence terminus after the first prediction, when the predefined generation length is 128. We then compare this confidence between two empirically defined groups of problems: those answered and those answered correctly only with much longer
correctly in under 128 tokens (length-suffiicas sequence (length-ing insufficient). The visualization clearly shows that the model predicts EOS tokens sufficient problems. indicating awareness that the current length is adequate. Conversely, for problems where the length insufficient, the model utilizes the available space more thoroughly, resulting in lower EOS confidence the sequence's
with significantly higher confidence for the length-sufficient
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terminus. If the model deems the current length inadequate to fully articulate its response, it will tend to utilize all available space, making it less likely to generate EOS tokens with high confidence.
Based on this insight, introduce preliminary length estimation loop that precedes the main denoising process. As depicted in Figure 3 (b), this loop begins with a short initial generation length. In each estimation iteration, the model performs forward pass the current sequence (prompt plus [MASK] tokens) to evaluate its predictions. We specifically focus on a window at the end of the sequence and calculate the model's average confidence predicting the EOS token in these positions. If this confidence falls below predefined threshold, we interpret this as a "length insufficient" signal. This indicates that the model, being forced conclude prematurely. has not yet formed a complete response and is therefore unwilling to commit to an EOS token. In response, we expand the generation length by appending additional [MASK] tokens to the end of the sequence. This length adjustment loop repeats until the EOS confidence surpasses the threshold or maximum length limit is reached. Through this mechanism, DAEDAL dynamically allocates a task- appropppop generation length for the model before commencing the fine-grained denoising process.
2.2.2 ITERATIVE MASK INSERTION
After the first stage allocates an appropriate length for the task, the second stage of DAEDAL, Iterative Mask Insertion, further enhances generation flexibility during the denoising process. We propose that predictions with exceptionally low confidence are not merely signals of uncertainty; on a deeper level, they indicate that the local context too constrained articulate a complex thought or logical step. In other words, this is signal that the model requires more "discursive space" to refine its reasoning.
Therefore, during each denoising step, in addition to identifying and filling high-confidence tokens, our method also flags the masked position with the lowest prediction confidence, provided it falls below very low threshold. As shown Figure (b), this position is not treated as merely "difficult token" but is marked an "cxpansion point". When position is marked for expansion, instead of simply remasking it, we dynamically replace the single [MASK] token with block multiple [MASK] tokens. This operation effectively inserts additional space into the sequence.
This mechanism provides the model with "breathing room" precisely where complex reasoning or detailed description is needed, allowing it to better structure its language and logic in subsequent denoising iterations. Unlike the first stage, which performs a holistic length adjustment, Iterative Mask Insertion localized, on-demand refinement that occurs in real-time during generation. This enables DAEDAL to handle complex scenarios where the required length exceeds the initial length estimate, significantly enhancing the model's expressive ability.
3 EXPERIMENTS
3.1
IMPLEMENTATION DETAILS
We LLaDA-Instruct-S8 and LLaDA-1.5-8B as baseline models. To demonstrate the gener- alization ability of DAEDAL, we also conducted experiments on Dream-Instruct-78 For faimess, did not any acceleration caching optimization methods proposed in subsequent works. Crucially, DAEDAL employs the exact same hyperparameters across all experiments and models, with model-specific tuning. Detailed configurations are provided in Appendix E.
3.2 BENCHMARKS AND METRICS
To comprehensively evaluate the effectiveness of DAEDAL, we conducted experiments on four benchmarks spanning the domains of mathematical reasoning and code generation. For mathematical reasoning, we utilize GSM8K (Cobbe et al., 2021), which consists of grade-school math word problems to assess multi-step reasoning, and the more challenging MATH500 (Lightman et al., 2023) benchmark, composed of competition-level mathematics problems; performance by accuracy. To evaluate code generation, we employ MBPP (Austin et al., 2021b) benchmark for eetry Python tasks and the more complex, handwritten HumanEval (Chen et al., 2021) benchmark to test program synthesis capabilities. For these code generation tasks, we report the pass @ 1 metric assess the correctness of the generated code in single attempt.
both is measured
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Table Main Results DAEDAL on LLaDA-Instruct-ba We compare the baseline performance at various generation lengths (64 to 2048) against DAEDAL. Acc denotes accuracy, Etoken is the average effective tokens (the response length excluding trailing padding), Noken is the average total tokens, and Eratio is the effective token ratio. The best configuration for the baseline is highlighted in orange. The best results are bold and underlined, and the second-best results are underlined.
Fixed-Length Denoising (Baseline)
DAEDAL
Benchmark
Metric
64
128
256
512
1024
2048
64
Aec
48.0
67.9
77.6
83.3
83.8
82.6
85.8
Etoken
62
124
234
287
284
294
267
GSMSK
Ntoken
64
128
256
512
1024
2048
363
Eratio
97.1%
97.0%
91.2%
56.0%
27.7%
14.4%
73.5%
Acc
24.0
29.0
35.6
38.8
39.4
39.6
44.2
Etoken
62
123
245
424
583
718
541
MATH500
Ntoken
128
256
512
1024
2048
704
Eratio
96.4%
96.4%
95.8%
82.8%
56.9%
35.1%
76.8%
Acc
20.8
28.0
37.4
38.2
37.4
38.8
40.8
Etoken
122
232
331
335
336
324
MBPP
Ntoken
64
128
256
512
1024
2048
618
Eratio
95.1% %
95.7%
90.6%
64.7%
32.7%
16.4%
52.5%
Aec
18.9
26.2
36.0
47.0
47.6
47.0
48.2
Etoken
60
125
245
471
641
669
523
HUMANEVAL
Ntoken
64
128
256
512
1024
2048
813
Eratio
93.2%
97.6%
95.6%
92.0%
62.6%
32.7%
64.3%
Average Ace
27.93
37.78
46.65
51.83
52.05
52.00
54.75
3.3 MAIN RESULTS
We conducted a comprehensive evaluation on four benchmarks. The results for LLaDA IInDD---Inttut-881 -L.DA-1.5-8B. and Dream- comparing the Fixed-Length Denoising baseline against our DAEDAL method, are presented Table 1, Table (Appendix F), and Table 6 (Appendix G), respectively. For the baseline models, performance is highly dependent manually tuning the generation length for each specific task. We therefore report baseline performance across six fixed- length configurations, from 64 to 2048 tokens. In addition to accuracy (Acc), introduce three key metrics: the total number of tokens generated (A (Ntoken), which for the baseline is its preset fixed length; the number of effective tokens used to answer the question (Eroken). representing the "net" response length after removing trailing EOS padding; and the effective token ratio (Eratio).
DAEDAL achieves strong performance with a unified initial length. The results clearly demon- strate the superior performance of DAEDAL. Despite starting with a short initial length, DAEDAL's two-stage length adjustment and expansion mechanism allows baselines with the same initial length, but also achieve performance that is comparable, and in some cases superior, to the peak performance of the meticulously tuned fixed-length baseline. This finding highlights the effectiveness of DAEDAL and exposes the inherent impracticality of the fixed-length paradigm, as the optimal length for the baseline varies across different benchmarks, underscoring the necessity of dynamic length adaptation. To visually illustrate this dynamic adaptability, Figure contrasts the distribution of generation lengths (Ntoken) used by DAEDAL against the single fixed length of the best best-performing baseline for each benchmark. Unlike the baseline, which is constrained to pre-defined length for all problems, DAEDAL automatically adapts its generation length on a per-prot per-problem basis, resulting in diverse length distribution that reflects varying task complexity.
only significantly outperform
DAEDAL adaptively finds the optimal generation length. Further analysis reveals that DAEDAL intelligently estimates and generates responses of an appropriate length. In most cases, the number

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Published as a conference paper at ICLR 2026
1310
500
Number of Questions
Number of Questions
50
40
DAEDAL (85.8 8 on SSMBK)
The pest-performing Baseline (93.8 65MBK)
20
Best-perfarming Baseline
(39.6 MATH MATH500) -
30
DAEDAL (44.2 on MATH500)
20
30
10
656
2048
64
646-
512
1024
2048
Length of Model's Responses
Length of Model's Responses
500
Nomber of Questions
Number of Questions
20
Best-perfarming Baseline (38.8 MBPP)
10
Best-parforee
(47.6 on HUMANEVAL)
DAEDAL HUMANEVAL)
DAEDAL (40.8 MBPP)
(48.2
10
0
64...256
512
1074
2046
64..2256
512
1024
2048
Length of Madel's Responses
Length of Madel's Responses
The figure compares the distribution of total tokens used per problem by DAEDAL (orange histogram) and the baseline (blue histogram) across four benchmarks. DAEDAL's dynamic, per-problem adaptation results in a varied distribution of lengths. In contrast, the baseline is constrained single fixed length for all problems within benchmark, represented by single bar in its histogram.
Figure 4: Distribution of individual Response Lengths (Nooken) ) on
of effective tokens (Ezoken) produced by DAEDAL is comparable to that of the baseline's best- performing configuration. This suggests that DAEDAL adaptively finds the model's inherent "sweet spot" for the token length required by given task. The baseline's behavior corroborates this: when the fixed length is set beyond its optimal point, performance degrades even though the number of effective tokens may continue increase. DAEDAL's adaptive nature effectively avoids this performance decay from over-expansion.
DAEDAL significantly improves computational efficiency. Furthermore, DAEDAL offers signifi- cant efficiency advantages. While achieving superior accuracy, the total number of tokens (Neoken) generated by DAEDAL is generally lower than that of the baseline at its peak-perfont peak-performance setting. A similar effective token count with lower total token count results in much higher effective token ratio (Fratso). This dramatically improves the utilization of the computational resource by reducing the overhead of bidirectional attention unnecessarily long sequences and minimizing wasted resources on generating meaningless padding tokens.
3.4 ANALYSIS ON DAEDAL
DAEDAL's two stages are individually effective and synergistic when combined. As shown in Table 2, conducted an ablation study to analyze the individual contributions of DAEDAL's two core components: Initial Length Adjustment (Stage 1 only) and Iterative Mask Insertion (Stage only). The results indicate that applying either stage individually yields significant performance gains over the baseline. The full DAEDAL method, which combines both stages, ultimately achieves the best performance, surpassing the results of using either stage alone. This demonstrates that the two stages are complementary and indispensable for achieving optimal results.
DAEDAL's stages highlights the importance of the initial length for global planning. When using the Iterative Mask Insertion (Stage 2) stage in isolation, we observe that its performance is sensitive the initial length. When starting from a very short initial length (c.g.. 64), its performance, while substantially better than the baseline at that same length, still falls short of the bascline's 's peak performance achieved at an optimal, longer length. Yet, when initiated with a more reasonable length (e.g., 256), this stage alone can surpass the baseline's 's overall best result. This behavior is expected and highlights the nature of the second stage as a local, on-demand expansion mechanism. If the initial length is severely constrained, the DLLM's ability to form sound global plan for the solution is compromised from the outset. While subsequent local expansions can provide remedies, the overall performance ceiling still limited by the flawed initial plan. This not only demonstrates the effectiveness of Iterative Mask Insertion but also underscores the necessity of Initial Length Adjustment (Stage 1) for establishing solid foundation for global planning.
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Table 2: Ablation Results of DAEDAL's Two Stages. Experiments are conducted on GSM8K with
We compare the performance of the full DAEDAL method, as well as its individual stages (Stage and Stage 2), against the baseline. The baseline is evaluated multiple fixed lengths, with its best configuration highlighted in orange. Stage 1 and DAEDAL evaluated with an initial length of 64, while Stage is evaluated with varying initial lengths (64, 128, 256).
Fixed-Length Denoising (Baseline)
w/ Stagel
w/ Stage2
DAEDAL
Metric
128
256
512
1024
2048
64
256
64
Ace
48.0
67.9
77.6
83.3
83.8
82.6
84.1
72.3
81.1
84.7
85.8
Etoken
62
124
234
287
284
294
253
127
167
256
267
Nioken
64
128
256
512
1024
2048
311
152
209
340
363
Fratio
97.1%
97.0%
91.2%
56.0%
27.7%
14.4%
81.3%
83.1%
79.7%
75.3%
73.5%
Table 3: Ablation Results on DAEDAL's Initial Length. Experiments are conducted on GSMSK and HUMANEVAL using LLaDA-Instruct-S- DAEDAL is evaluated with initial lengths ranging from 32 to 512. We highlight our default setting (Linse = 64) in blue.
GSM8K
HUMANEVAL
Metric
32
64
128
256
512
32
64
128
256
512
Acc
85.8
85.8
85.8
85.8
85.1
48.2
48.2
48.2
48.2
48.2
Btoken
267
267
268
277
523
523
523
523
523
Ntoken
363
363
363
369
532
813
813
813
813
816
Erutio
73.5%
73.5%
73.5%
72.7%
52.0%
64.4%
64.4%
64.4%
64.4%
64.1%
Table 4: Ablation Results on DAEDAL's Expansion Factor and EOS Confidence Window Size. Both ablation studies conducted GSM8K using I.LaDA-Instruct-B8 The left panel shows results for varying Focect ranging from 8 to 32, and the right panel for varying Weos ranging from 32. We highlight our default setting foetor and W =3o)=32) in blue.
Expansion Factor (E (acto)))
EOS Confidence Window Size (W (Weoe)
Metric
Metric
16
24
32
8
16
24
32
Acc
85.8
85.8
86.4
86.3
Acc
82.9
83.5
84.4
85.8
Etoken
267
272
272
274
Etoken
232
252
260
267
Ntoken
363
386
392
405
Ntoken
289
327
347
363
Eratio
73.5%
70.5%
69.3%
67.6%
Eratio
80.2%
77.1%
75.0%
73.5%
DAEDAL exhibits strong robustness to the initial length. A core advantage of DAEDAL is its ability to achieve strong performance from short, unified initial length. We conduct an ablation study to verify its robustness to this hyperparameter, Linit As shown in Table 3, DAEDAL delivers remarkably stable performance across wide range of initial lengths, from 32 512 tokens. On GSM8K is
HumanEval, the accuracy remains identical across all settings, while the variation negligible. This result demonstrates that DAEDAL is largely insensitive the initial length setting. It confirms that users do not need to meticulously tune this hyperparameter; unified and short initial length (e.g., 64) is sufficient achieve optimal performance, fulfilling its original design objective.
DAEDAL's performance is insensitive to the expansion factor granularity. We conduct an ablation study on the expansion factor, which controls the number of [MASK] tokens added during single expansion event. As shown in the left panel of Table 4, DAEDAL's performance remains remarkably stable across a range of expansion factors (8 to 32). This result suggests that the specific granularity of each expansion step is not critical. smaller factor leads to more frequent, fine- gne-gnained expansions, while a larger factor results in fewer, coarser expansions. Regardless of the step size, the model robustly converges to similar, task-appropriate total length. This finding further corroborates
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Thigh
Teos
Best-perree Dest-performing Setting
85
Baseline with =.24
0.95
0.70
Baseline with =
80
Baseline with =26
75
0.90
70
Baseline with L
0.85
0.50
65
60
0.80
0.40
55
0.05
0,10
0.15
0,20
0.60
0,70
0.80
0,90
Tlow
Texpand
50
Baseline with Same Lim
Figure 5: Ablation Results on DAEDAL's Thresholds. The two 4x4 heatmaps present a grid search over two interdependent threshold pairs: (Thigh: Tlow) and (Teas: Terpand). All 32 configurations were evaluated on GSM8K using The color bar also provides reference color for performance of baseline. Our default settings are in blue boxes. The results demonstrate remarkable stability, with all configurations comparable to the best-performing baseline, and some even outperforming
Higher accuracy is indicated by darker green.
our core hypothesis that the model possesses strong internal estimate of the length required for given problem, and DAEDAL's mechanism effectively enables the model reach that target.
DAEDAL is robust to the EOS confidence window size, achieving optimal performance with large window. We analyze the sensitivity the EOS confidence window size, used to determine length sufficiency. As shown in the right panel of Table 4, performance is stable for larger window sizes but degrades for very small values. Notably, even this sub-optimal configuration still yields significant gains over the baseline at comparable short initial length (77.3 vs. 48.0 Acc on GSM8K when I6).= We attribute the performance drop at small window sizes higher probability of misjudging the model's intent. A larger window provides more robust signal by averaging confidence over a wider context. contrast, a small window susceptible to localized fluctuations and may prematurely terminate length expansion based few high- nigh-confidence EOS tokens the sequence's 's immediate end, leading to length
and a drop in final performance.
DAEDAL demonstrates broad robustness across its threshold settings. We conduct DAEDAL:
comprehensive ablation study on all the four key threshold hyperparameters
Teus, Texpand: Thigh Tlow We analyze these in interdependent pairs via a grid search: (Thigh Tow which govern token-level filling and expansion decisions. Specifically, the high-confidence threshold Thigh analogous the confident decoding strategy proposed in Dimple, which aims accelerate inference by simultaneously filling all tokens that exceed certain confidence level. The second pair, (Teoe: Texpand) controls sequence-leve length adjustments. The results on GSM8K, presented in Figure 5, demonstrate DAEDAL's exceptional robustness. Across the 32 tested configurations. all configurations are comparable to the best- rest-performing baseline (83.8 Acc), with some even outper- forming it, and the overall performance variation across all settings is minimal. This indicates that DAEDAL is largely insensitive to the precise choice of these thresholds, confirming that it can deliver strong and stable performance without requiring extensive hyperparameter tuning.
CONCLUSION
In this work, we addressed fundamental architectural constraint of Diffusion Large Language Models (DLLMs): the reliance on a statically predefined generation length. This limitation hinders their practical application by creating difficult dilemma: insufficient lengths cripple performance on complex tasks, while excessive lengths not only incur significant computational overhead but can sometimes lead performance degradation. We introduced DAEDAL, novel training-free two-stage denoising strategy that resolves this issue by leveraging the model's own internal signals. DAEDAL first performs Initial Length Adjustment set coarse, task-appropriate budget, and then uses Iterative Mask Insertion to dynamically expand the sequence at regions requiring more detailed reasoning during the denoising process. Our extensive experiments and analyses demonstrate that

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Published as a conference paper at ICLR 2026
DAEDAL successfully endows DLLMs with the ability for dynamic, per-problem length adaptation. This allows a model starting from a short, unified initial length to achieve performance that is comparable, and at times superior, to that of meticulously tuned fixed-lengle baselines. By removing the need for manual length tuning and enabling the model to find its own optimal response length, DAEDAL not only enhances performance but also improves computational efficiency. Ultimately, this work bridges critical capability gap between diffusion and autoregressive models, paving the way for more flexible, efficient, and capable non-autoregressive language generation.
ACKNOWLEDGMENTS
This project is funded in part by Shanghai Artificial Intelligence Laboratory, Shanghai Innovation Institute, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin a PI of CPI under the InnoHK.
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A REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide a detailed algorithmic description of DAEDAL in Appendix D. All hyperparameters and hardware specifications used experiments are explicitly listed in Appendix E. The source code for reproducing our main results has been included in the Supplementary Materials.
B THE USE OF LARGE LANGUAGE MODELS
We utilized Large Language Models (LLMs), such as Google's s Gemini, as a writing assistance tool. The use of the LLM was strictly limited to improving the clarity, conciseness, and grammatical cor- rectness of the text. Specific applications included proofreading for typographical errors, rephrasing complex sentences to enhance readability, and ensuring consistent terminology throughout the paper.
C RELATED WORKS
Diffusion Large Language Models. In recent years, Diffusion Language Models (DLLMs) have emerged as prominent area of research. Among them, LLaDA (Nie et al., 2025) stands out as the first large-scale diffusion model trained from scratch to reach the billion-parameto scale. Trained on 2.3 trillion tokens, LLaDA-SB has demonstrated performance competitive with state-of- the-art autoregressive models like LLaMA-3-4 (Grattafiori et al., 2024). Indeed, autoregressive architectures have long served as the dominant foundation across diverse modalities, ranging from standard text generation complex streaming video understanding and real-time interaction (Zhang et al., 2023; Qian et al., 2024a; Ding et al., 2024; Qian et al., 2024b: Zhang et al., 2024; Qian et al., 2025). LLaDA proves the remarkable scalability and potential of the native diffusion architecture on multiple tasks (Hendrycks et al., 2020; Suzgun et al., 2022: Cobbe et al., 2021). Subsequently, LLaDA-1.5 (Zhu al., 2025) advanced this paradigm by successfully applying reinforcement learning for preference alignment, achieving significant further improvements benchmarks for mathematics, code, and alignment. In contrast to LLaDA, another line of research has explored adapting existing AR LLMs into DLLMs. For instance, models like DiffuLLaMM (Gong et al., 2024) and Dream (Ye et al., 2025) were developed by fine-tuning pre-trained
L.MSs such as GPT2 (Radford et al., 2019), LLaMA2 (Touvron et al., 2023) and Qwen (Yang et al., 2024). While these adapted models have also achieved strong results, our work focuses more on native, from-scratch DLLMs like LLaDA, seeking to explore their generation mechanisms and address the specific challenge of fixed-length inference.
Inference Strategies for DLLMs. Existing research on inference strategies for DLLMs has predomi- nantly focused on enhancing generation speed through computational optimizations. For example, Fast-dLLM (Wu et al., 2025) introduces a novel block-wise approximate Key- Value (KV) Cache tailored for bidirectional attention models, combined with confidence-aware parallel decoding strategy, to achieve up to an improvement throughput. Similarly, M-Cchee (Liu et al., 2025) observes the static nature of prompts and the dynamic sparsity of responses during DLLM inference. proposing an adaptive caching framework that combines long-interval prompt caching with partial response updates to achieve lossless speedup. Dimple (Yu et al., 2025) proposes "Confident Decod- ing" strategy that dynamically adjusts the number of tokens generated at each step based on model confidence, thereby significantly reducing the total number of iterations. In summary, while all these methods (Ma et al., 2025; Israel et al., 2025; Ben-Hamu et al., 2025) have made significant strides in improving the inference speed of DILMS via computational caching and parallel decoding. They do not address the more fundamental issue that the generation length itself needs to adapt dynamically to different task requirements. To our knowledge, the problem of dynamically adjusting and expanding the total generation length of DILMs at inference time remains unexplored. Our work, therefore, aims to fill this critical gap by proposing a novel dynamic adaptive expansion strategy.
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D PSEUDO-CODE OF DAEDAL
Algorithm The DAEDAL Inference Process
Prompt c, model fo. initial/max length Linit/ manz thresholds Teas; Thigh, Tlow; Terpands expansion factor factor, EOS confidence window size Weo.
Input:
2: Output: Generated sequence
Stage 1: Initial Length Adjustment
3: x +
[c. [MASK]
[MASK]
> Initialize sequence
Linit
4:
while length(x) < Leman do
5: Laggiss f- [x(()
6: confeos f- CompoteeFOSConfieceeeLnneee Weos)
7: if confeos < Teos then
8:
[MASK] [MASK]
Expand length if EOS confidence is low
Fustor
9:
else
10: break
> length is sufficient, exit loop
11:
end
end while
D Stage 2: Iterative Denoising and Mask Insertion
while ContainsMusk(xx do
14:
Lioghts & fx)
Poont; 4- GeeCConfideneenAndPPeiiiifie
[nggits)
16:
Mmmsked t- {i x; [MASK]]
Iall f {i
Masked Poonf, > Thigh }
Identify high-confidence set for filling
18:
Icandidates {i E Mmasked A Paont,s < Tlow
Find low-confidence candidate positions
19:
for j E Iau do
20:
X; 4 Xj
Fill in high-confidence tokens
21:
end for
22:
confeas CoompteFFOSCoffddencee Tologits W
23:
if confe.. < Texpand A length(x) < Tomax A (eaaddidates then
Texpand f- arg minieTontteee Poonf, Replace Xizspus with [MASK),... [MASK] Expand sequence at the selected position
Select the position with the lowest confidence
25:
Eactor
26:
end if end while
27:
return
E EXPERIMENTS CONFIGURATION
All experiments were conducted on a server equipped with 8 NVIDIA A800 80G GPUs with the batch size set 8. We employ single, universal of hyperparameters models, requiring
all experiments and model-specific tuning. Specifically, the initial and maximum lengths are set to Linst 64 and Lmaz 2048. The thresholds are configured as Teas 0.5, Thigh 0.9. Now 0.1. and Terpand = 0.9. The expansion factor is Ejactor 8, and the EOS confidence window size is W coos 32. We also demonstrate DAEDAL's robustness to these choices in Section 3.4, which provides a detailed ablation study on these hyperparameters.
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F MAIN RESULTS OF DAEDAL ON LLADA-1.5-8B
Table Main Results of DAEDAL on LLaDA-1.5-8B. We compare the baseline performance at various generation lengths (64 to 2048) against DAEDAL.. Acc denotes accuracy, Etoken is the average effective tokens (the response length excluding trailing padding). Nooken is the average total tokens, and Bratio is the effective token ratio. The best configuration for the baseline is highlighted in orange. The best results are bold and underlined, and the second-best results are underlined.
Fixed-Length Denoising (Baseline)
DAEDAL
Benchmark
Metric
64
128
256
512
1024
2048
64
Acc
49.4
71.0
80.4
83.7
83.8
84.5
85.5
Etoken
62
125
237
293
287
292.5
275
GSM8K
Ntoken
64
128
256
512
1024
2048
377
Bratio
97.4%
97.5%
92.6%
57.2%
28.15
4.3%
73.0%
Acc
23.2
29.6
35.4
40.2
43.6
40.2
42.4
Etoken
62
125
246
429
584
717
588
MATH500
Ntoken
64
128
256
512
1024
2048
743
Eratio
96.9%
97.5%
96.2%
83.7%
57.1% %
35.0%
75.2%
Acc
20.6
30.2
39.2
38.6
39.8
39.6
40.2
Etoken
61
124
237
352
344
356
342
MBPP
Nooken
128
256
512
1024
2048
645
Eratio
95.1 %
96.6%
92.7%
68.7%
33.6%
17.4%
53.0%
Acc
18.3
22.0
37.8
45.1
49.4
50.0
48.8
Etoken
125
251
475
677
754
561
HUMANEVAL
Ntoken
64
128
256
512
1024
2048
848
Eratio
94.0%
97.7%
98.2%
92.9%
66.1
36.8%
66.2%
Average Ace
27.88
38.20
48.20
51.90
54.15
53.58
54.23
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Published as a conference paper at ICLR 2026
G MAIN RESULTS OF DAEDAL ON DREAM-INSTRUCT-7E
Table 6: Main Results of DAEDAL on Dream-Instruct-7B. We compare the baseline performance various generation lengths (64 to 2048) against DAEDAL.. Ace denotes accuracy. Ptoken the average effective tokens (the response length excluding trailing padding). Noken is the average total tokens, and Bratio is the effective token ratio. The best configuration for the baseline is highlighted in orange. The best results are bold and underlined, and the second-best results are underlined.
Fixed-Length Denoising (Baseline)
DAEDAL
Benchmark
Metric
64
128
256
512
1024
2048
64
Acc
20.8
51.9
81.3
83.7
83.8
83.7
84.2
Etoken
63
123
202
217
217
232
209
GSM8K
Ntoken
64
128
256
512
1024
2048
326
Bratio
98.0%
96.3%
78.9%
42.3%
21.2%
1 11.3%
63.9%
Acc
15.2
30.6
40.8
44.8
45.2
44.0
49.2
Etoken
62
122
223
334
422
632
371
MATH500
Ntoken
64
128
256
512
1024
2048
540
Eratio
97.5%
95.0%
87.2%
65.3%
41.2%
30.8%
68.7%
Acc
25.6
39.6
50.4
47.4
49.0
50.0
49.4
Etoken
60.3
116.2
182.4
228.5
252.2
294.4
190.7
MBPP
N Noken
128
256
512
1024
2048
493
Eratio
94.2%
90.8%
71.3%
44.6%
24.6%
14.4%
38.7%
Acc
7.3
30.5
51.8
57.3
51.8
55.5
53.7
Etoken
123
196
269
284
338
272
HUMANEVAL
Ntoken
64
128
256
512
1024
2048
572
Eratio
75.5%
96.2%
76.8%
52.6%
27.7%
16.5%
47.5%
Average Ace
17.22
38.14
56.08
58.31
57.45
58.30
59.12
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H VISUALIZATIONS OF THE DLLM'S AWARENESS OF LENGTH SUFFICIENCY
(a) 6SM8K (128)
(b) GSM8K (256)
(c) GSM8K (512)
(d) MATH500 (128)
0.20
0.00
(e) MATH500 (256)
-0.20
-0.40
(f) MATH500 (512)
(g) HUMANEVAL (128)
(h) HUMANEVAL (256)
(i) HUMANEVAL (512)
Figure Visualization of the DLLM's awareness of length sufficiency. The heatmaps show the difference in average EOS token confidence at the sequence terminus, measured after the first prediction on fully masked token input (length range from 128 to 512). This difference is the result of subtracting the average confidence on length-insuffiiien problems (those answered correctly only with much longer sequence) from that on length-sufficient problems. The predominantly green color (difference > 0) indicates that EOS confidence is higher for length-sufficient problems, validating our core insight.
17


Reviews:
[{"review_id": "sjzXJ8ICQ5", "reviewer": "Reviewer_xU9B", "summary": "This paper proposes an inference-time method for extending the generation length of text diffusion models. Starting with a small canvas, masked tokens get added until EOS occurs with high-enough probability at the end of the sequence. Given this initial sequence of masked tokens, the method alternates between filling in high-confidence tokens and adding more tokens based on EOS confidence. Tokens are added to the lowest confidence mask positions, and are expanded by a constant block size.\n\nResults on simple math and coding evaluations show that the method obtains reasonable accuracy while adaptively determining response length. The average ratio between utilized tokens versus padding is around 65% across all tasks.", "questions": "The paper supports its claims. The main improvement would be to include speed-accuracy comparisons to other accelerated methods, namely speculative decoding.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The method seems effective and is very simple. The presentation was written to be reader-friendly, but I believe would benefit from including more details. For example, Algorithm 1 has a few more details than Fig 3. The results are reasonable, with easing the need for manual sequence length tuning.", "weaknesses": "The main weakness is that the main baselines to compare to is block diffusion or other adaptive-length methods. The sell of variable-length  diffusion would likely have to be a speed increase while preserving accuracy, or another point on the speed-accuracy Pareto frontier. It is possible that spec-decoded autoregressive models achieve better speeds at similar accuracy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jb764FCz6S", "reviewer": "Reviewer_owRq", "summary": "This paper introduces an inference method for a pretrained masked diffusion model that supports length variability. In particular, the algorithm leverages two core operations-determining length using EOS confidence and then expanding iteratively for the masked position with the lowest prediction confidence. Experiments have been conducted on several math and code benchmarks on LLaDA-Instruct and Dream. The results show that the approach can maintain the accuracy of the \"optimal\" length sweeping over several lengths while being adaptive-length at inference time.", "questions": "1. I am curious about the generalization of the findings in Figure 2 on over datasets and number of tokens (other than 128). Would the authors be able to provide more empirical justifications?\n\n2. In the main table, it would be helpful to add actual wall-clock decoding time as a metric. Would the authors provide results for this?\n\n3. Would the approach benefit from some training/finetuning of the model to better account for the variable-length objective?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The goal of achieving adaptive-length decoding is promising and important to obtain better trade-off frontier between sample quality and latency.\n\n2. The proposed approach is easy to follow and the method requires no additional training.\n\n3. The presentation of the paper is clear and the empirical performance, specifically on the accuracy v.s. total tokens, is quite impressive. The ablation studies including hyperparameter sensitivity analysis are informative.", "weaknesses": "1. The proposed idea of using confidence of predicting EOS token to determine the length of the sequence seems a bit heuristic. A more comprehensive evaluation should be conducted on more datasets and tasks to justify the effectiveness of such heuristic.\n\n2. Efficiency metrics such as wall-clock time/latency is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ex5oJPl30k", "reviewer": "Reviewer_XwxZ", "summary": "This paper tackles a central limitation of Diffusion LLMs—the need to pre-set a fixed output length—and proposes DAEDAL, a training-free, two-stage inference method that first estimates a task-appropriate length by checking EOS confidence at the sequence end and then expands low-confidence regions on the fly via mask insertion during denoising. On LLaDA-Instruct-8B and related DLLMs, DAEDAL matches or exceeds carefully tuned fixed-length baselines while using tokens more efficiently (e.g., GSM8K 85.8 vs. 83.8 accuracy with a much higher effective-token ratio), and extensive ablations show robustness to initial length, thresholds, and expansion factors.", "questions": "Please see Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The proposed method is simple and intuitively reasonable. Requiring no retraining is a plus\n- The proposed method demonstrates solid empirical improvements over the best-tuned fixed-length baselines on math reasoning and code generation benchmarks (e.g., MATH500), while generating effective tokens more efficiently.\n- This paper conducts a thorough analysis of key hyperparameters of the method, showing its robustness to different configurations.", "weaknesses": "- While the paper shows in experiments that a combination of the two expansion stages gives the best performance, in principle, there still lacks a clear reason why both stages are necessary. It's natural to consider merging the first length adjustment stage into the second dynamic expansion stage. Interestingly, according to Table 2, stage 1 already contributes to most of the performance improvement. I think more investigation should be put into this.\n- The ablation study shows the method's robustness to threshold hyperparameters, but it's only for a single model-dataset pair, i.e., LLaDA-Instruct-8B on GSM8K. I wonder whether the threshold findings can be transferred to other model-dataset combinations. Or do you need to tune thresholds again for a different dataset?\n- It would be better to have some more direct measurements on the actual inference speed.\n- The benchmarks focus on math and code. How would the proposed method perform on general language tasks?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)