Diffusion Language Model Knows the Answer Before It Decodes
Authors: Pengxiang Li
Yefan Zhou
Dilxat Muhtar
Lu Yin
Shilin Yan
Li Shen
Yi Liang
Soroush Vosoughi
Shiwei Liu
Venue: ICLR 2026 Oral (2026)
Keywords: diffusion language model
discrete
Primary area: generative models

Abstract: Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and flexible token orders. However, their inference remains slower than that of autoregressive models, primarily due to the cost of bidirectional attention and the large number of refinement steps required for high-quality outputs.
In this work, we highlight and leverage an overlooked property of DLMs—**early answer convergence**: in many cases, the correct answer can be internally identified by half steps before the final decoding step, under both semi-autoregressive and random remasking schedules. 
For example, on GSM8K and MMLU, up to 97\% and 99\% of instances, respectively, can be decoded correctly using only half of the refinement steps.
Building on this observation, we introduce **Prophet**, a training-free fast decoding paradigm that enables **early commit decoding**. Specifically, Prophet dynamically decides whether to continue refinement or to go "all-in" (i.e. decode all remaining tokens in one step), using the confidence gap between the top-2 prediction candidates as the criterion. It integrates seamlessly into existing DLM implementations, incurs negligible overhead, and requires no additional training.
Empirical evaluations on LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3.4$\times$ while preserving high generation quality, and yields additional speedups when combined with existing acceleration methods.
These results recast DLM decoding as a problem of *when to stop sampling*, and demonstrate that early answer convergence provides a simple yet powerful mechanism for accelerating DLMs on reasoning, code, and planning tasks with identifiable answer regions. Our code is available at \url{https://github.com/pixeli99/Prophet}.

Published as a conference paper at ICLR 2026
DIFFUSION LANGUAGE MODELS KNOW THE ANSWER BEFORE DECODING
Pengxiang Li"; Yefan Zhou²; Dilxat Muhtar Lu Yin', Shilin Yan, Li Shen' Soroush Vosoughi", Shiwei Shiwwel Liu567
'The Hong Kong Polytechnic University DDatmmouth College
UUniversity Surrey *Sun Yat-sen University
'ELLIS Institute Tübingen
MPI for Intelligent Systems Tübbingen Al Center
ABSTRACT
Diffusion language models (DLMs) have recently emerged as an alternative autoregressive approaches, offering parallel sequence generation and flexible token orders. However, their inference remains slower than that of autoregressive models, primarily due to the cost of bidirectional attention and the large number of refinement steps required for high-quality outputs. In this work, highlight and leverage an overlooked property of BLMs-earbh answer convergence: in many cases, the correct answer can be internally identified by half steps before the final decoding step, under both ammi-autoregressive and random remasking schedules. For example, on GSM8K and MMLU, up to 97% and 99% instances, respectively, can be decoded correctly using only half of the refinement steps. Building on this observation, we introduce Prophet, a training-free fast decoding paradigm that enables early commit decoding. Specifically, Prophet dynamically decides whether to continue refinement or to go "all-in" (i.e. decode all remaining tokens in step), using the confidence gap between the top-2 prediction candidates as the criterion. It integrates seamlessly into existing DL.M implementations, incurs negligible overhead, and requires additional training. Empirical evaluations on LLaDA-8B and Dream-77 7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3,4x while preserving high generation quality, and yields additional speedups when combined with existing acceleration methods. These results recast DLM decoding as problem of when to stop sampling, and demonstrate that early answer convergence provides a simple yet powerful mechanism for accelerating DLMs on reasoning, code, and planning tasks with identifiable answer regions. Our code is available at ftthtps:
INTRODUCTION
Along with the rapid evolution of diffusion models in various domains (Ho et al. 2020 Nichol &
Dhariwal 202T Ramesh al. 2021 Saharia al. 2022; Ting al.
2022). Diffusion language
models (DLMs) have emerged as compelling and competitively efficient alternative to autoregres-
sive (AR) models for sequence generation (Austin et 202 Lou al. 2023 Shi et al. 2024 Sahoo et al. 2024 Nie et al. 2025 Gong et 2024 Ye et al. 2025). Primary strengths of DLMs over AR models include, but are not limited to, efficient parallel decoding and flexible generation orders. More specifically, DLMs decode all tokens in parallel through iterative denoising and re- masking steps. The remaining tokens are typically refined with low-confidence predictions over successive rounds (Nie et al. 2025).
Despite the potential speedup of DLMs, the inference speed of DLMS is slower than that of AR models in practice, due to the lack of KV cache mechanisms and the significant performance degra- dation associated with fast parallel decoding (Israel et al. (025a).. Recent efforts have proposed excellent algorithms to enable the KV cache (Ma et al. [025a]]]] Liu et al. 2025a Wu et al. 2025 and improve the performance of parallel decoding (Wu et al. 2025 Wei et al. 2025a aa Hu et al. 2025).
"Equal contribution.

---

Published as a conference paper at ICLR 2026
In this paper, aim accelerate the inference of DLMs from different perspective, motivated by an overlooked yet powerful phenomenon of DLMs early answer convergence. Through extensive analysis, observed that: a strikingly high proportion of samples can be correctly decoded during the early phase of decoding for both semi-autoregressive remasking and random remasking. This trend becomes more significant for random remasking. For example, on GSM8K and MMLU, up to 97% and 99% of instances, respectively, can be decoded correctly using only half of the refinement steps.
Motivated by this finding, we introduce Prophet, a training-free fast decoding strategy designed capitalize on early answer convergence. Prophet continuously monitors the confidence gap be- tween the top-2 answer candidates throughout the decoding trajectory, and opportunistically decides whether is safe decode all remaining tokens at once. By doing so, Prophet achieves substantial inference speedup (up to 3,4x) while maintaining high generation quality. Our contributions are threefold:
Empirical observations of early answer convergence: We demonstrate that strikingly high proportion of samples (up to 99%) can be correctly decoded during the early phase of decoding for both autiiauutorersssvve remasking and random remasking. This underscores a fundamental redundancy in conventional full-length slow decoding.
A fast decoding paradigm enabling early commit decoding: We propose Prophet, which eval- uates at each step whether the remaining answer is accurate enough to be finalized immediately, which we call Early Commit Decoding. We find that the confidence gap between the top-2 answer candidates serves as an effective metric to determine the right time for early commit decoding, Us- ing this metric, Prophet dynamically decides between continued refinement and immediate answer emission.
Substantial speedup gains with high-quality generation: Experiments across diverse reason- ing, code, and planning benchmarks with identifiable answer regions reveal that Prophet delivers up to 3,4x reduction in decoding steps. Crucially, this acceleration incurs negligible degradation accuracy-affirming that early commit decoding is not just computationally efficient but also semantically reliable for DLMs.
RELATED WORK
2.1
DIFFUSION LARGE LANGUAGE MODEL
The idea of adapting diffusion processes to discrete domains traces back the pioneering works Sohi--DDiett al. (0115) Hoogeboom et al. (2021). A general probabilistic framework was later developed in D3PM (Austin et al.) 2021a), which modeled the forward process as a discrete- state Markov chain progressively adding noise to the clean input sequence over time steps. The verse process is parameterized to predict the clean text sequence based on the current noisy input by maximizing the Evidence Lower Bound (ELBO). This perspective was subsequently extended the continuous-time setting. Campbell et al. (2022) reinterpreted the discrete chain within a continuous- time Markov chain (CTMC) formulation. An alternative line of work, SEDD (Lou et al.) 2023), focused on directly estimating likelihood ratios and introduced a denoising score entropy criterion for training. Recent analyses in MDLM (Shi al. 2024 Sahoo et al., 2024 Zheng et al. 2024 and
RADD Yu et al. 2024) demonstrate that multiple parameterizations of MDMs are in fact equivalent.
Motivated by these groundbreaking breakthroughs. practitioners have successfully built product- level DLMs. Notable examples include commercial releases such as Mercury (Labs et al.) 2025). Gemini Diffusion (DeepMind) (025), and Seed Diffusion (Song et al. (025b), as well as open-source implementations including LLaDA (Nie et al., (025)) and Dream (Ye et al.) (0255) However, DLMs face an efficiency-
tradeoff that limits their practical advantages. While DLMs can theoretically decode multiple tokens per denoising step, increasing the number of simultaneously decoded tokens results in degraded quality. Conversely, decoding a limited number of tokens per denoising step leads to high inference latency compared AR models, DLMs cannot naively leverage key-value (KV) caching or other advanced optimization techniques due to their bidirectional nature.
2

---

Published as a conference paper at ICLR 2026
2.2 ACCELERATION METHODS FOR DIFFUSION LANGUAGE MODELS
Recent efforts to accelerate DLM inference while maintaining quality span several directions.
KV Cache optimization. One line of work exploits the high similarity between hidden states across consecutive denoising steps to enable approximate caching (Ma et al.
2025b Liu et al. 2025b Hu
ct al. 2025). related strategy restructures the denoising process in a cemi-auuoregrresivve or block- autoregressive manner, caching states from previous context or blocks, optionally with periodic
cache refreshing (Wu et al. 2026 Arriola et
2025
Wang al. 2025b Song al. 2025a).
Token pruning. A second direction reduces attention cost by pruning redundant tokens (Xiao et al. 2026 Chen et al., 2026a). For example, DPad (Chen et al.
2026a) is a training-free method that treats suffix tokens as computational scratchpad and prunes distant ones prior to computation.
Sampling and decoding optimization. A third direction optimizes the sampling or decoding process, either by increasing the number of tokens decoded per step or by reducing the total number of denoising steps. Training-based approaches include reinforcement learning (Song et al.) (0256) and distillation (Wang et al., 2025a Chen et al., (026b)). Our work is most closely related to the training-free free approaches in this direction. Fast-dLLM (Wu et al.) 2026) combines approximate block-wise KV caching with confidence-und awwrre parallel decoding that commits tokens exceeding a confidence threshold. SlowFast (Wei et al.] (025a) proposes a dynamic scheme that alternates between cautious exploratory phase and an accelerated phase guided by three principles: certainty, convergence, and position. WINO (Song et al. (025a) introduces a revokable draft-and-verinn process that aggressively drafts multiple tokens and remasks low-confidence ones via auxiliary shadow block. Other relevant work studies better criteria and metrics for inference-time decisions, including analyses of denoising dynamics (Wei et 2025b Huang & Tang. 2025), alignment with small autoregressive models (Israel et al. 2025b), and speculative decoding using the DLM itself as a draft model (Agrawal al. 2025).
Positioning of our study. The primary contribution of this work is to identify. formalize, and validate Early Answer Convergence, a fundamental property of the DLM denoising trajectory. The proposed method Prophet operationalizes this property by treating decoding an optimal stopping problem over the answer region, deciding when to terminate further refinement based on answer confidence. Unlike methods such Fast-dLLM that optimize the cost per step, Prophet reduces the total number of steps required. The two approaches are therefore orthogonal and can be combined for multiplicative speedups, supported empirically in later sections. We note that Early Answer Convergence has been independently discovered in concurrent work (Wang et al., (025a);; however, their focus is on averaging predictions across time steps improve accuracy, whereas develop an early commit decoding method that reduces computational cost while maintaining quality.
3 PRELIMINARY
3.1 BACKGROUND ON DIFFUSION LANGUAGE MODELS
Concretely, let 2o (aa)( be clean input sequence. At an intermediate noise level t [0, 7). we denote by xe the corrupted version obtained after applying a masking procedure to a subset of its tokens.
Forward process. The corruption mechanism can be expressed as a Markov chain
T
q xD) = II
g(x, 2z-1),
(I)
t=1
which gradually transforms the original sample zo into maximally degraded representation Tr. At each step, additional noise is injected, so that the sequence becomes progressively more masked as t increases.
While the forward process in Eq. is straightforward. its exact reversal is typically inefficient be- cause it unmasks only one position per step (Campbell et al.. 2022 Lou et al. (023). To accelerate generation, a common remedy is to use the r-leaping approximation (Gillespie (001). which en- ables multiple masked positions be recovered simultaneously. Concretely, transitioning from
3

---

Published as a conference paper at ICLR 2026
corruption level t to earlier level t can be approximated
1,
24 7 MASK] 2¹. =
0:1: II Aspe (2t, mt), quje(as z4) =
f: A 20 (2² xt);
2. MASK]. -
24 [MASK] [MASK]
(2)
Here, (0) 122) is a predictive distribution over the vocabulary, supplied by the model itself, whenever a masked location is to unmasked. In conditional generation (e.g.. producing a response To given a prompt p), this predictive distribution additionally depends on p, i.e. (op((r Fe. p).
Reverse generation. To synthesize text, one needs to approximate the reverse dynamics. The generative model parameterized as
pa (Forr) po(er) IE po mx) mx) II 1 20) po (T))
This reverse process naturally decomposes into two complementary components. (1) Prediction step. The model (r .z) attempts to reconstruct a clean sequence from the corrupted input level We denote the predicted sequence after this step by z/. i.e. z = pa pa(za 21). (2) Remasking step. Once a candidate reconstruction vn is obtained, the forward noising mechanism is reapplied in order to produce a partially corrupted sequence x;-- that is less noisy than zt. This "remasking" can be implemented various ways, such as masking tokens uniformly at random or selectively masking low-confidence positions (Nic et al.] (025). Through the interplay of these two steps- steps-prediction prediction and remasking the model iteratively refines an initially noisy sequence into a coherent text output.
3.2 EARLY ANSWER CONVERGENCY
In this section, we investigate the early emergence of correct answers in DLMs. We conduct com- prehensive analysis using LLL.DA-BB (Nie et al., (025) on two widely used benchmarks: GSM8K (Cobbe et al. 2 and MMLU (Hendryeks et al., 202T). Specifically, we examine the decoding dynamics, that is, how the top-1 predicted token evolves across positions at each decoding step, and report the percentage the full decoding process at which the top-1 predicted tokens first match the ground truth answer tokens. In this study, we only consider samples where the final output contains the ground truth answer.
For low confidence remasking, we set Answer length at 256 and Block length 32 for GSM8K, and Answer length at 128 and Block length to 128 for MMLU, For random remasking, we set Answer length at 256 and Block length 256 for GSM8K, and Answer length at 128 and Block length at 128 for MMLU. We present the results of GSM8K in Figure [ and the MMLU results in Appendix B
I. A high proportion of samples can be correctly decoded during the early phase of decoding. Figure Ia demonstrates that when remasking with the low-confidence strategy, 24.2% samples already correctly predicted in the first half steps, and 7.9% samples can be correctly decoded in the first 25% steps. These two numbers will be further largely boosted 97.2% and 88.5%, when shifted to random remasking as shown in Figure Ic
II. Our suffix prompt further amplifies the early emergence of correct answers. Adding the suffix prompt "Answer:" significantly improves early decoding. With low confidence remasking, the proportion of correct samples emerging by the 25% step rises from 7.9% 59.7%, and by the 50% step from 24.2% to 75.8% (Figure 1b). Similarly, under random remasking, the 25% step proportion increases from 88.5% to 94.6%. We clarify that the suffix prompt acts as a semantic anchor rather than introducing oracle information. Since DLMs generate bidirectionally, this anchor explicitly conditions the model to locate the solution in the designated region, reducing the search space and accelerating convergence.
III. Decoding dynamics of chain-of-thought tokens. We further examine the decoding dynamics of chain-of-thougge tokens in addition to answer tokens, shown in Figure 2 First, most non- answer tokens fluctuate frequently before being finalized. Second, answer tokens change far less often and tend to stabilize earlier, remaining unchanged for the rest of the decoding process.
4

---

Published as a conference paper at ICLR 2026
Number of Samples
125
Number of Samples
500
1.0% eljamplss aet corre rede aoceessnn sanner by decolinng steps
24.25 samples RCE correct neswer byy deceding suggs
58.7% sumples
15.8% comple ourres answer by deceding stops
100-
400
75
300
50
200
25
100
20
60
80
100
40
60
100
First Correct Answer Emergence (% Total Decoding Steps)
First Correct Answer Emergence (% of Total Decoding Steps)
(a) w/o suffix prompt (low-confidence remasking) (b) w/ suflix prompt (low-confidence remasking)
Number of Samples
150
Number of Samples
500-
dearpple 25% decsding steps
samuples correct 50% deceding steps
somples 50% dece-ing stepe
by
400
25% decolig
100
300
50
200
20
40
60
80
100
0
20
40
60
80
100
First Correct Answer Emergence (% Total Decoding Steps)
First Correct Answer Emergence (% of Total Decoding Steps)
w/o suffix prompt (random remasking)
suffix prompt (random remasking)
Figure I: Distribution of early correct answer detection during decoding process.. Histograms show when correct answers first emerge during diffusion decoding, measured as percentage total decoding steps, using LLaDA 8B an GSM8K. Red and orange dashed lines indicate 50% and 70% completion thresholds, with corresponding statistics showing substantial early convergence. Suffxx prompting (b.d) dramatically accelerates convergence compared to standard prompting (a.c). This early convergence pattern demonstrates that correct answer tokens stabilize as top-1 candidates well before full decoding.
4 METHODOLOGY
Based on the above findings, we introduce Prophet, a training-free fast decoding algorithm designed to accelerate the generation phase of DL.Ms Prophet by committing to all remaining tokens one shot and predicting answers soon as the model's predictions have stabilized, which we call Early Commit Decoding. Unlike conventional fixed-step decoding, Prophet actively monitors the model's certainty at each step to make an informed, on-the-fly decision about when to finalize the generation.
Confidence Gap as a Convergence Metric. The core mechanism of Prophet is the Confidence Gap. simple yet effective metric for quantifying the model's 's conviction for given token. Let Ngen be the total generation length. semi-autorese aunreeuuoreeressivee decoding, tokens are generated in blocks of size Notock- Prophet focuses monitoring the Answer Region A of length Nas. within the current generation window. At any decoding step I,, the DLM produces logit matrix L, E N where N is the sequence length and V is the vocabulary size. For each position i, we identify the highest logit value, ( and the second-bighest, L The confidence gap St. is defined as their difference:
(4) This value serves as robust indicator of predictive certainty. A large probability gap signals that the prediction has likely converged, with the tap-ranked token clearly outweighing all others. We caleu- late the average confidence gap 9t exclusively over the answer region A (i.e., Gt- TAT Eie Ht,)) rather than the entire sequence, maximize sensitivity.
gr.i 1 L (2)
Early Commit Decoding. The decision of when to terminate the decoding loop can be framed as an optimal stopping problem. At each step, must balance two competing costs: the computa-
5

---

Published as a conference paper at ICLR 2026
No Change
Token Change
Token Decoded
Correct Answer Token
100
Outper Tokeen Positico Index
110-
Outpat Token Position Index
190-
120-
200
125-
205
130-
135-
215-
145-
M
225-
155
180
300
Decoding Steps
Decoding Steps
(a) w/o suffix prompt
(b) w/ suffix prompt
Figure 2: Decoding dynamics across all positions based on ceakimum--roobbbllie predictions. Heatmaps track how the top-1 token changes at each position, if it is decoded at the current step, over the course decoding. (a) Without our suffix prompts, correct answer tokens reach maxi- mum probability that the model internally identifies correct answers well before the final output. Results are shown for LLaDA 8B solving problem index 700 from GSM8K under low-confidence decoding. Gray indicates positions where the top-1 prediction remains unchanged, orange marks positions where different token, blue denotes the step which the corresponding y-axis
step 119. (b) With suffix prompts, this occurs earlier at step 88, showing
the prediction changes position is actually decoded, and green box highlights the answer region where the correct answer remains stable as the top-1 token and can be safely decoded without further changes the decoding process progresses.
(a) Standard Full-Step Decoding Chaig-of-Thought
[HASK]
3x3e9,
Output: 540
t=B
1u70
Answer Tokens
Redundant Steps
540
(b) Prophet with Early Commit Decoding
55% Steps Saved
[PASK]
[NASK]
[NASK]
sprints (NASK]
3+3+9, 9-60- [MASK]
3x3+9, 9-60-540
Output:
Confidence Gap >
540
Figure 3: An illustration of the Prophet's ' early-commit-decodinn mechanism. (a) Standard full-step decoding completes all predefined steps (e.g., 10 steps), incurring redundant computations after the answer has stabilized (at t=6). (b) Prophet dynamically monitors the model's confidence (the "Con- fidence Gap")). triggers early commit decoding as soon the answer converges, saving a sig- nificant portion of the decoding steps (in this case, 55%) without compromising the output quality.
tional cost of performing additional refinement iterations versus the risk of error from premature and potentially incorrect decision. The computational cost is a function of the remaining steps, whereas the risk of error is inversely correlated with the model's predictive certainty, for which the Confidence Gap serves as a robust proxy.
Prophet addresses this trade-off with adaptive strategy that embodies principle of time-varying risk aversion. Let denote = (Tmax t) TT/Tman as the decoding progress, where Tmax the total number of decoding steps, and (p) is the threshold for early commit decoding. In the early noisy

---

Published as a conference paper at ICLR 2026
stages of decoding (when progress p is small), the potential for significant improvement in prediction high. Committing to an answer at this stage carries a high risk. Therefore, Prophet acts risk-averse manner, demanding an exceptionally high threshold (Thigh) to justify early commit decoding, ensuring that such a decision is unequivocally safe. As the decoding process matures (as p increases), two things happen: the model's predictions stabilize, and the potential computational savings from stopping early diminish. Consequently, the cost of performing one more step becomes negligible compared the benefit of finalizing the answer. Prophet thus becomes more risk-tolerant, requiring progressively smaller threshold (Tow) to confirm convergence.
This dynamic risk aversion policy is instantiated through our staged threshold function, which maps the abstract trade-off between inference speed and generation certainty onto concrete decision rule:
Thigh
if p 0.33
gr 7()), where r(p)
Timid
if 0.33 S p < 0.67
(5)
Too
if 2 0.67
Once the exit condition is satisfied at step t", the iterative loop is terminated. The final output is then constructed in single parallel operation by filling any remaining [MASK] tokens with the argmax of the current logits Lt..
Algorithm Summary. The complete Prophet decoding procedure is outlined in Algorithm I In- tegration of the confidence gap check adds negligible computational overhead to the standard DLM decoding loop. Prophet is model-agnostic, requires retraining, and can be readily implemented as a wrapper around existing DLM inference code.
Algorithm Prophet: Early Commit Decoding for Diffusion Language Models
1: Input: Model Mo. prompt Xprmpi, max steps Trux total generation length Ngen
2: Input: Threshold function T(-), answer region A (where |.|| Nans)
3: Initialize sequence XT < concat( Xpompe; [MASK]*0n)
4: Let Mr be the set of masked positions step t.
5: for = Tmax: Timax 1, 1..... ., do
6: Compute logits: L, (x:)
7:
& Prophet's Carly-Commit-Decodin Check
8: Calculate average confidence gap I. over positions A using Eq. 4
9: Calculate progress: + (Tmax TT/Tmux
if gt r()) then
Check condition from Eq.
11:
&o - argmax dim = -1)
Xg x, Fill positions in M, with tokens from Xo-
13:
Return Xg
b Terminate and finalize
end
15:
>> Standard DLM Refinement Step Determine tokens to unmask U4 S M, via a remasking strategy.
16:
17: &o argmax(Le, dim = -1)
18:
Update x < X,, replacing tokens at positions 44 with those from Sy-
19:
end for
20:
Return Xo
D Return result after full iterations if no early commit decoding
5 EXPERIMENTS
We evaluate Prophet DLMs to validate two key hypotheses: first, that Prophet can preserve the performance of full-budget decoding while using substantially fewer denoising steps; second, that our adaptive approach provides more reliable acceleration than naive static baselines. We demonstrate that Prophet achieves notable computational savings with negligible quality degradation through comprehensive experiments across diverse benchmarks.
5.1 EXPERIMENTAL. SETUP
We conduct experiments on two ofate-of-the-art diffusion language models: LLaDA-8B (Nie et al. 2025) and Dream-7B (Ye al. 2025). For each model, compare two decoding strategies: Full

---

Published as a conference paper at ICLR 2026
Table I: Benchmark results on LLaDA-8B-Instree and
We report Accuracy
(%) for both Full-step decoding and Prophet. The numbers in parentheses indicate the Accuracy Gain (A) compared the baseline. Sudoku and Countdown are evaluated using 8-shot setting; all other benchmarks use zero-shot evaluation. Detailed configuration is listed in the Appendix C
LLaDA-SB
Dream-7B
Benchmark
Full (%)
Prophet (A)
Speedup
Full (%)
Prophet (A)
Speedup
General Tasks
MMLU
54.1
54.0 (-0.1)
x
67.6
66.1 (-1.5)
2.47
ARC-C
83.2
83.5 (+0.3)
1.88x
88,1
87.9 (-0.2)
2.61x
Hellaswag
70.9 (+2.2)
2.14x
81.2
81.9 (+0.7)
2.55
TruthfulQA
34.4
46.1 (+11.7)
2.31x
55.6
53.2 (-2.4)
1.83x
WinoGrande
73.8
70.5 (-3.3)
1,71
62.5
62.0 (-0.5)
1.45x
PIQA
80.9
81.9 (+1.0)
1.98x
86.1
86.6 (+0.5)
2.29x
Mathematics & Scientific
GSM8K
77.1
77.9 (+0.8)
1.63x
75.3
75.2 (-0.1)
1.71x
GPQA
25.2
25.7 (+0.5)
1.82x
27.0
26.6 (-0.4)
1.66x
Code Generation
HvmanEval
30.5
30.5 (0.0)
1.20x
$4.9
55.5 (+0.6)
1.44x
MBPP
37.6
37.4 (-0.2)
1.35x
54.0
54.6 (+0.6)
1.33x
Planning Tasks
Countdown
15.3
15.3 (0.0)
2.67 x
14.6
14.6 (0.0)
2.37x
Sudoku
35.0
38.0 (43.0)
2.46x
89.0
89.0 (0.0)
3.40
the standard diffusion decoding with the complete step budget of Tmax and Prophet employs early commit decoding with dynamic threshold scheduling. The threshold parameters are set to Thigh 7.5, Tiid 5.0, and New = 2.5, with transitions occurring 33% and 67% of the decoding progress. These hyperparameters were selected through preliminary validation experiments.
Our evaluation spans four capability domains to comprehensively assess Prophet's effectiveness. For general reasoning. we use MMLU (Hendrycks et al., (02T), ARC-Challenge (Clark et al.] (2018). HellaSwag (Zellers al., (019), TruthfulQA (Lin et al., (021), WinoGrande (Sakaguchi et al. 202T), and PIQA (Bisk et al. 2020). Mathematical and scientific reasoning are evaluated through GSM8K (Cobbe al., (221] and GPQA (Rein al., (023). For code generation, we employ HumanEval (Chen et al. 2022) and MBPP (Austin et al. (2221b). Finally, planning capabilities are assessed using Countdown and Sudoku tasks (Gong et al. 2024). We follow the prompt in simple-evals for LLaDA and Dream, making the model reason step by step. Concretely, we set the generation length to 128 for general tasks, to 256 for GSMSK and GPQA, and to 512 for the code benchmarks. Unless otherwise noted, all baselines use a number of iterative steps equal to the specified generation length. All experiments employ greedy decoding to ensure deterministic and reproducible results.
5.2
MAIN RESULTS AND ANALYSIS
The results of our experiments are summarized in Table [ Across the general reasoning tasks, Prophet demonstrates its ability match or even exceed the performance of the full baseline. For ex- ample, using ..LDA-8BB Prophet achieves 54.0% on MMLU and 83.5% on ARC-C, both of which are statistically on par with the full step decoding. Interestingly, on HellaSwag, Prophet (70.9% not only improves upon the full baseline (68.7%) but also the half baseline (70.5%), suggesting that early commit decoding can prevent the model from corrupting an already correct prediction in later, noisy refinement steps. Similarly, Dream-7B maintains competitive performance across benchmarks, with Prophet achieving 66.1% % on MMLU compared to the full model's 67.6% a minimal drop of 1.5% while delivering 2.47 x speedup.
Prophet continues to prove its reliability in more complex reasoning tasks, including mathematics, science, and code generation. For the GSM8K dataset, Prophet with LLaDA-8B obtains accuracy
8

---

Published as a conference paper at ICLR 2026
of 77.9%, outperforming the baseline's 77.1%. This reliability also extends to code generation benchmarks. For instance, on HumanEval, Prophet perfectly matches the full baseline's is score with LLaDA-8B (30.5%) and even slightly improves it with Dream-7B (55.5%
54.9%). Notably, the acceleration on these intricate tasks (e.g.. 1.20x on HumanEval) is more conservative compared to general reasoning. This demonstrates Prophet's adaptive nature: it dynamically allocates more denoising steps when a task demands further refinement, thereby preserving accuracy on complex problems. This reinforces Prophet's role "safe" acceleration method that avoids the pitfalls of premature, static termination.
In summary, our empirical results strongly support the central hypothesis of this work: DLMs often determine the correct answer long before the final decoding step. Prophet successfully capitalizes on this phenomenon by dynamically monitoring the model's predictive confidence. It terminates the iterative refinement process soon as the answer has stabilized, thereby achieving significant computational savings with negligible, and in some cases even positive, impact on task performance. This stands in stark contrast to static truncation methods, which risk cutting off the decoding process prematurely and harming accuracy. Prophet thus provides a robust and model-agnostic solution to accelerate DLM inference, enhancing its practicality for real-world deployment.
5.3 COMPARISON WITH OTHER ACCELERATION METHODS
We compare Prophet with two DLM acceleration methods, demonstrating that Prophet is orthogonal to and compatible with both.
Comparison with distillation-based acceleration. We compare against Self- Desf-Distillation Through Time (Deschenaux & Gulcehre 2025) (SDTT), a training- crasenng-based method that reduces the number of inference steps. We implement a preliminary version of SDTT on
distill- ing standard 256-step teacher into a 128-step student model (2x acceleration) and evaluate on GSM8K. As shown in Table 2a] SDTT effectively maintains accuracy (76.9%) while halving infer- ence steps. Prophet Prophet achieves competitive 1.63 speedup with slightly higher accuracy (77.9%), without any retraining cost. Most importantly, our results demonstrate that Prophet and SDTT are orthogonal and complementary. Crucially, Prophet and SDTT are complementary: ap- plying Prophet's early-exit strategy top of the SDTT-distilled student (Row 4) yields 3.21 speedup with negligible accuracy drop, demonstrating that distilled models retain the early answer convergence property that Prophet exploits.
Comparison with KV cache-based acceleration. We compare against L. (Wu et al. 2026), training-free method that reduces per-step computation via approximate KV caching and parallel decoding. We apply Prophet's early-exit mechanism on top of Fast-dLLM using LLaDA-8B on GSM8K. As shown in Table 2b while Fast-dLLM alone achieves a 6.82x speedup by reducing per-step cost, combining it with Prophet yields a 7.66x total speedup without quality degradation. This multiplicative effect arises because the two methods operate on orthogonal dimensions: Fast- dLLM reduces the cost per step, while Prophet reduces the total number of steps required.
Table 2: Comparison between Prophet and acceleration baselines GSM8K.
(a) Comparison with SDTT.
(b) Comparison with Fast-dLLM.
Method
Accuracy (%)
Steps (Avg)
Speedup
Method
Accuracy (%)
Speedup
LLaDA (Teacher)
77.1
256
1.00x
Baseline (LLaDA-SB)
77.1
1.00x
SIVT (Distilled)
76.9
2.00x
Fast-dl.I M (KV Cache Parallel)
76.6
6.82x
Prophet (Ours)
77.9
160
1.63x
Prophel (Ours)
77.9
1.63x
SIYT Prophet
3.21x
Fast-dLLMM Prophet
77.3
7.66x
5.4 ABLATION STUDIES
Beyond the coarse step budget ablation above, we further dissect why Prophet outperforms static truncation by examining (i) sensitivity to the generation length L and available step budget, (ii) compatibility with different remasking heuristics, and (iii) robustness to the granularity of semi- autoregressive block updates. Together, these studies consistently show that Prophet's adaptive carly-commit rule improves the compute-quality Pareto frontier, whereas static schedules either under-compute (hurting accuracy) over-compute (wasting steps).

---

Published as a conference paper at ICLR 2026
Table Ablation study on step budget and remasking strategy.. (a) Accuracy vs. step budget un- der two generation lengths L. Prophet stops early (average steps in parentheses) yet matches/exceeds
the full-budget baseline. (b) Accuracy under different remasking strategies; Prophet complements token-selection policies.
(a) Accuracy vs. step budget and generation length.
(b) Remasking strategy.
Step Budget (T
Strategy
Baseline
Ours (Prophet)
L
16
32
128
Prophet (Avg Steps)
Full
Random
63.8
66.6
256
7.7
22.5
58.8
76.2
77.9 (=160)
77.1
Low-confidence
71.3
72.7
128
21.8
50.3
67.9
71.3
72.7 (F:74
71.3
Top-k margin
72.4
73.1
Table 4: Sensitivity to block length on GSM8K beemi-autoregressiig updates). brittle to coarse-grained updates and yields larger gains as block length increases.
Prophet is less
Block length
16
32
64
128
Baseline
67.1
68.7
71.3
59.9
33.1
Ours (Prophet)
72.8
73.3
72.7
69.8
52.2
A (Abs.)
+5.7
+4.6
+1.4
49.9
+19.1
Accuracy vs. step budget under different L. Table 3a sammummzzzess GSM8K accuracy as we vary the number of refinement steps under two generation lengths (T. = 256 and I, 128). Accuracy under static step cap rises monotonically with more steps (e.g., 7.7% - 22.5% + 58.8% - 76. for 16/32/64/128 at I. 256), but still underperforms either the full-budget decoding or Prophet. In contrast, Prophet stops adaptively at R 160 steps for L - 256 (saving 38% steps; 256/ 160 ss 1.63x) and yields a higher score than the 256-step baseline (77.9% vs. 77.1%). When the target length is shorter (L = 128), Prophet again surpasses the 128- step baseline (72.7% vs. 71.3%) while using only = 74 steps (saving s 42%; 128/74 1.73x). These results reaffirm that the gains are not byproduct of simply using fewer steps: Prophet avoids late-stage over-refinement when the answer has already stabilized, while still allocating extra iterations when needed.
Remasking strategy compatibility. Table 3b evaluates three off-the-shelf remasking heuristics (random, low-confidence, top-k margin). Prophet consistently outperforms their static counterparts, with the largest gain under random remasking (+2.8 points), aligning with our earlier observation that random schedules accentuate early answer convergence. The improvement persists under more informed heuristics (low-confidence: +1.4; top-k margin: +0.7), indicating that Prophet's stopping rule complements, rather than replaces, ooken-selection policies.
Granularity of semi-autoregressive refinement (block length). Table 4 shows that static block schedules are brittle: accuracy peaks around moderate blocks and collapses for large blocks (e.g., 59.9 at 64 and 33.1 at 128). Prophet markedly attenuates this brittleness, delivering consistent gains across the entire range, and especially large blocks where over-aggressive parallel updates inject more noise. For instance, at block length 64 and 128, Prophet improves accuracy by +9.9 and 119.1 points, respectively. This robustness is direct consequence of Prophet's time-varying risk-aversion: when coarse-grained updates raise uncertainty, the threshold schedule defers early commit; once predictions settle, Prophet exits promptly to avoid additional noisy revisions.
CONCLUSION
In this work, we identified and leveraged a fundamental yet overlooked property of diffusion lan- guage models: early answer convergence. Our analysis revealed that up to 99% of instances can be correctly decoded using only half of the refinement steps, challenging the necessity of conven- tional full-length decoding. Building on this observation, we introduced Prophet, training- free early commit decoding paradigm that dynamically monitors confidence gaps to determine optimal termination points. Experiments on LLaDA-8B and Dream-7B demonstrate that Prophet achieves a reduction of up to 3.4x reduction in decoding steps while maintaining generation quality. By recasting DLM decoding as an optimal stopping problem rather than fixed-budget iteration, our work opens new avenues for efficient DLM inference in tasks with identifiable answer regions and suggests that early convergence a core characteristic of how these models internally resolve un- certainty, across diverse tasks and settings.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Sudhanshu Agrawal, Risheek Garrepalli, Raghavv Goel, Mingu Lee, Christopher Lott, and Fatih Porikli. Spiffy: Multiplying diffusion IIm acceleration via lossless speculative decoding, 2025.
URL https:
abs 2509 18085
Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang. Zhixuan Qi, Jiaqi Han, Sub- ham Sekhar Sahoo, and Volodymyr Kuleshov. Block diffusion: Interpolating between autoregres- sive and diffusion language models, 2025. URL
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in neural information processing systems, 34:1 14:1:99 1991-17993, 2021a.
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry. Quoe Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.0777 07732, 2021b.
Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali Thabet, and Jonas K Kohler. Judge decoding: Faster speculative sampling requires going beyond model alignment. In The Thirteenth Interna- tional Conference on Learning Representations.
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical com- monsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, 2020.
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet. A continuous time framework for discrete denoising models. Advances in Neural Information Processing Systems, 35:28266-28277 2022.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 2021.
Xinhua Chen, Sitao Huang, Cong Guo, Chiyue Wei, Yintao He, Jianyi Zhang, Hai Helen Li, and Yiran Chen. DPad: Efficient diffusion language models with suffix dropout. In The Fourteenth International Conference on Learning Representations, 2026a.
Zigeng Chen, Gongfan Fang. Xinyin Ma, Ruonan Yu, and Xinchao Wang. dparallel: Learnable parallel decoding for dLLMs. In The Fourteenth International Conference Learning Repre- sentations, 2026b.
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. 1rX1v.1803.05457vv, 2018
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2 14160, 2021.
Google DeepMind. Gemini-diffusion, 2025. URL peoogleeddeepmmn//ggmmiii
ffusion/
Justin Deschenaux and Caglar Gulcehre. Beyond autoregression: Fast LLMMs via self-distillation through time. In The Thirteenth International Conference Learning Representations, 2025.
Daniel T Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of chemical physics, 1 11((4):116-1733, 2001.
Shansan Gong, Shivam Agarwal, Yizhe Zhang, Jiacheng Ye, Lin Zheng, Mukai Li, Chenxin An, Peilin Zhao, Wei Bi, Jiawei Han, et al. Scaling diffusion language models via adaptation from autoregressive models. arXiv preprint arXiv:2410. 17891, 2024.

---

Published as a conference paper at ICLR 2026
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXhv:2103.03887 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling, Argmax flows and multinomial diffusion: Learning categorical distributions. Advances in Neural Information Processing Systems, 34:12454-1224 2021.
Zhanqiu Hu, Jian Meng, Yash Akhauri, Mohamed S Abdelfattah, Jac-sun Sco, Zhiru Zhang, and Udit Gupta. Accelcrating diffusion language model inference via efficient kv caching and guided diffusion. arXiv preprint arXiv:2505.2146) 2025.
Chihan Huang and Hao Tang. Ctrldiff: Boosting large diffusion language models with dynamic
block prediction and controllable generation. arXiv preprint arXiv:2505 2025.
Daniel Israel, Guy Van den Broeck, and Aditya Grover. Accelerating diffusion 1lms via adaptive parallel decoding. arXiv preprint arXiv:2506. 00413, 2025a.
Daniel Israel, Guy Van den Broeck, and Aditya Grover. Accelerating diffusion llms via adaptive parallel decoding, 2025b. / a25p:///bb//206.000m 00413
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola. Torsional dif- fusion for molecular conformer generation. Advances in neural information processing systems, 35:24240 24253,, 2022.
Inception Labs, Samar Khanna, Siddhant Kharbanda, Shufan Li, Harshit Varma, Eric Wang, Sawyer Birnbaum, Ziyang Luo, Yanis Miraoui, Akash Palrecha, Stefano Ermon, Aditya Grover, and Volodymyr Kuleshov. Mereury: Ultra-fast language models based on diffusion, 2025. URL
https: //tr:v///axivvva/ org/ abcg//bbs/505... 17298
Stephanie Lin, Jacob Hilton, and Owain Evans. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958, 2021.
Zhiyuan Liu, Yicun Yang, Yaojie Zhang. Junjie Chen, Chang Zou, Qingyuan Wei, Shaobo Wang, and Linfeng Zhang. dllm-cache: Accelerating diffusion large language models with adaptive caching. arXiv preprint arXiv:2506.0629) 2025a.
Zhiyuan Liu, Yicun Yang. Yaojie Zhang, Junjie Chen, Chang Zou, Qingyuan Wei, Shaobo Wang, and Linfeng Zhang. dllm-cache: Accelerating diffusion large language models with adaptive caching, 2025b. TRLL:ttss://axxii> abs/ 2306.000 95
Aaron Lou, Chenlin Meng, and Stefano Ermon. Discrete diffusion language modeling by estimating the ratios of the data distribution. arXiv preprint arXiv:23 10.66444, 2023.
Xinyin Ma, Runpeng Yu, Gongfan Fang, and Xinchao Wang. dkv-cache: The cache for diffusion language models. arXiv preprint arXiv:2505. 15781, 2025a.
Xinyin Ma, Runpeng Yu, Gongfan Fang, and Xinchao Wang. dKV-cache: The cache for diffusion language models. In The niith Annual Conference on Neural Information Processing Systems, 2025b.
hett/oorummidd-poooo.. JIHHs
Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International conference on machine learning, pp. 8162-8171. PMLR, 2021.
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. arXiv preprint 250.:25002009992. 2025.
Jingyang Ou, Shen Nie. Kaiwen Xue, Fenggi Zhu, Jiacheng Sun, Zhenguo Li. and Chongxuan Li. Your absorbing discrete diffusion secretly models the conditional distributions of clean data. arXiv preprint arXiv:2406.037:5 037336 2024.
12

---

Published as a conference paper at ICLR 2026
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Iyy Sutskever. Zero-shot foxt-to-image generation. In International conference on machine learning, pp. 8821-8831. Pmlr, 2021.
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Di- rani, Julian Michael, and Samuel Bowman. Gpqa: A graduate-level google-prose q&a bench- mark. arXiv preprint arXiv:2311 12022, 2023.
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image super-resolution via iterative refinement. IEEE transactions on pattrrn anal- ysis and machine intelligence, 45(4):4713-477) 2022.
Subham Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin Chiu, Alexander Rush, and Volodymyr Kuleshov. Simple and effective masked diffusion language models. Advances in Neural Information Processing Systems,
2024.
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adver- sarial winograd schema challenge at scale. Communications of the ACM, 64(9):99-106, 2021.
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K Titsias. Simplified and gener- alized masked diffusion for discrete data. arXiv preprint arXiv:2406.000 04329, 2024.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learn- ing, pp. 2256-2265, PMLR, 2015.
Yuerong Song, Xiaoran Liu, Ruixiao Li, Zhigeng Liu, Zengfeng Huang, Qipeng Guo, Ziwei He, and Xipeng Qiu. Sparse-dllm: Accelerating diffusion Ilms with dynamic cache eviction, 2025a. URL
https:/ /wtp///aavvvv...a//aa wwg/aba......oo......ee...eeeeee aalaa//s25000 2508 02558
Yuxuan Song, Zheng Zhang, Cheng Luo, Pengyang Gao, Fan Xia, Hao Luo, Zheng Li, Yuehang Yang, Hongli Yu, Xingwei Qu, Yuwei Fu, Jing Su, Ge Zhang, Wenhao Huang, Mingxuan Wang, Lin Yan, Xiaoying Jia, Jingjing Liu, Wei- Wei-Ying Ma, Ya-Qin Zhang, Yonghui Wu, and Hao Zhou. Seed diffusion: A large-scale diffusion language model with high-speed inference, 2025b. URL
https://erxxie org/ abs 2508 02193
Wen Wang, Bozhen Fang, Chenchen Jing, Yongliang Shen, Yangyi Shen, Qiuyu Wang, Hao Ouyang, Hao Chen, and Chunhua Shen. Time is a feature: Exploiting temporal dynamics in diffusion language models, 2025a. URL wwt.http::.. /tte:///axiiv..o wwt.:/s//ra/////../ wtt.bb///558000 09138
Xu Wang, Chenkai Xu, Yijie Jin, Jiachun Jin, Hao Zhang, and Zhijie Deng. Diffusion ilms can do faster-than-ar inference via discrete diffusion forcing, aug 2025b. Uttptttss///axxv
org//bs/ 2508. 09192 92 arXiv:2508.099 099192.
Qingyan Wei, Yaojie Zhang, Zhiyuan Liu, Dongrui Liu, and Linfeng Zhang. Accelerating dif- fusion large language models with slowfast: The three golden principles. arXiv preprint arXiv:2506. 10848, 2025a.
Qingyan Wei, Yaojie Zhang, Zhiyuan Liu, Dongrui Liu, and Linfeng Zhang. Accelerating diffusion large language models with slowfast sampling: The three golden principles, 2025b. htLpttp::
//axxiv.org/ab///22
10848
Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xic. Fast-dllm: Training- free acceleration of diffusion I1m by enabling kv cache and parallel decoding. arXiv preprint arXiv:2505.22615 2025.
Chengyue Wu. Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao. Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dLLM: shining-ffree acceleration of diffusion LLM by enabling KV cache and parallel decoding. In The Fourteenth International Conference on Learning Represen- tations, 2026.
Zhongyu Xiao, Zhiwei Hao, Jianyuan Guo, Yong Luo, Jia Liu, Jie Xu, and Han Hu. treaming- dllm: Accelerating diffusion Ilms via suffix pruning and dynamic decoding. arXiv preprint arXiv:2601. 17917, 2026.
13

---

Published as a conference paper at ICLR 2026
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7b: Diffusion large language models. arXiv preprint 3xiv:2508..54877 2025.
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can ma- chine really finish your sentence? arXiv preprint 1rXiv:1955007833 2019.
Kaiwen Zheng, Yongxin Chen, Hanzi Mao, Ming-Yo Yu Liu, Jun Zhu, and Qinsheng Zhang. Masked diffusion models are secretly time-agnostic masked models and exploit inaccurate categorical sampling. arXiv preprint arXiv:2409.02906 2024.
14

---

Published as a conference paper at ICLR 2026
APPENDIX
A DISCUSSION
Scope and applicability. Prophet designed for tasks with identifiable answer regions, such as mathematical reasoning, code generation, and planning. In such settings, early answer convergence provides reliable signal for termination. For open-ended generation tasks where the boundary between reasoning and answer is less distinct, applying Prophet presents non-trivial challenges, as the model may not exhibit clear convergence until late in the denoising trajectory. We therefore do not position Prophet as universal accelerator for all text generation, but rather demonstration that early answer convergence is a practically exploitable property in structured generation settings.
Conservative speedups complex tasks. Prophet exhibits more conservative speedups com- plex tasks such as code generation (1.20x on HumanEval) compared to short-answer tasks (3.40x on Sudoku). This reflects an intended behavior: in tasks where the answer is semantically dependent on preceding reasoning chains, Prophet correctly detects persistent uncertainty and defers termina- tion preserve correctness. Combining Prophet with trajectory compression methods such SDTT offers a natural solution, as distillation reduces the number of steps needed to form the reasoning chain, while Prophet further exploits convergence in the answer region. Our preliminary results confirm this, with SDTT + Prophet achieving a 3.21x x speedup on GSM8K compared to 1.63 for Prophet alone.
Learnable termination criteria. The confidence gap metric used in Prophet is intentionally simple: incurs negligible overhead and requires no additional training, making easy deploy. How- ever, for tasks where model confidence does not reliably correlate with correctness, a lightweight learnable judge could provide more robust termination signals. We view a "Judge Prophet", which replaces the heuristic confidence gap with trained discriminator, as a promising direction for future work, drawing on recent advances in judge-based decoding (Bachmann et al.).
Integration with system-level optimizations. As demonstrated in Section 5.3 Prophet is orthog- onal to both distillation-basee and cache-based acceleration methods. In KV Cache frameworks, Prophet's 's termination signal can be monitored by concatenating answer tokens with the active block, and once early commit is triggered, inference terminates immediately, saving the overhead of updat- ing the cache for remaining steps. We consider this system-level synergy compelling direction for future exploration.
B ADDITIONAL RESULTS
B.1
CORROBORATING RESULTS ON EARLY ANSWER CONVERGENCE
This section presents additional results on MMLU complementing the GSM8K results in the main paper Section 3.2 We investigate two remasking strategies, low-confidence remasking and random remasking, and examine the effect of suffix prompting on the distribution of decoding steps which correct answers first emerge.
15

---

Published as a conference paper at ICLR 2026
Number of Samples
200
15.1% cample set eltredeeesnnn esnee by
Number of Samples
99.7% camples act corrict assner by decoding steps
decolling steps
1500
150
21.4% of saappess cornect 50% deceding
99.0% ef sampless 50% decadlnng
100-
by
0
20
80
100
20
100
First Correct Answer Emergence (% Total Decoding Steps)
First Correct Answer Emergence (% Total Decoding Steps)
(a) MMLU w/o suffix prompt (low confidence)
(b) MMLU w/ suffix prompt (low confidence)
Number Samples
800
of Exmople 25% decrlling steps
Number of Samples
1500
sappps ceteenenioon 25% deceling strps
600-
99.25 of samples pct correct neswer by deceding stags
1000
9uef of camples get corrict oasher by decoding steps
400
500
200
20
40
60
80
100
20
40
60
80
100
First Correct Answer Emergence (% Total Decoding Steps)
First Correct Answer Emergence (% Total Decoding Steps)
(c) MMLU w/o suffix prompt (random)
(d) MMLU w/ suffix prompt (random)
Figure 4: Distribution of early correct answer detection during decoding process. Histograms show when correct answers first emerge during diffusion decoding, measured as percentage total decoding steps, using LLLDA 8R on MMLU. Red and orange dashed lines indicate 50% and 70% completion thresholds, with corresponding statisties showing substantial early convergence. Suffxx prompting (b.d) dramatically accelerates convergence compared to standard prompting (a.c). This early convergence pattern demonstrates that correct answer tokens stabilize as top-1 candidates well before full decoding.
B.2 QUALITATIVE ANALYSIS: DECODING TRAJECTORIES
To validate that DLMs identify answers before reasoning chains are complete, we present real de- coding trajectories in T In the "Simple Arithmetic" case, the model locks onto the answer "3" at only 50% of steps, while the intermediate calculation "2/2 = 1 is still masked. This confirms the efficacy of Prophet's 's early commit mechanism. Our observation is that DLMs often internally con- verge on the final result significantly earlier than they finalize the justification or Reasoning Chain. Therefore, when Prophet triggers early commit, the answer tokens are already stable and correct. While the intermediate COT region might retain some noise or lack the final polish of a full-step generation, have not observed significant degradations in the fluency or grammatical correctness of the final output.
16

---

Published as a conference paper at ICLR 2026
Table 5: Qualitative Analysis: Decoding Dynamies. Visualization of the decoding trajectory for simple arithmetic problem. Masked tokens are represented by (nst that sequences of con- secutive masks are abbreviated for visual clarity). Crucially, even when the intermediate reasoning chain is incomplete, the model locks onto the correct final answer early in the process (highlighted darker blue).
Stage
Decoded Sequence Stream (Case: Simple Arithmetic)
Early--10s seps)
To MASK MABK KASK add the Mask of blue MASK MASK the MASK of white MASE MASK MABK MKBK bolts MASK MASK Answer The answer
Prophet--50s steps)
To find the total number of bolts we need to add the MAsK of blue fiber
and the
amount of white fiber The robe takes 2 bolts of blue fiber It also takes half that much white fiber
KASK 3 bolts Final Answer The answer 3
Fullgoos stepe)
find the total number bolts we
to the amount
fiber
the amount of white fiber The robe takes 2 bolts of blue fiber also takes half much white liber so it takes 2
boll white fiber To the total
number of bolts we add the amount of blue fiber and the amount of white fiber 2 ( blue fiber 1 bolt white fiber 3 bolts Therefore the robe takes total
bolts Final Answer The answer
B.3 DISTRIBUTION OF INCORRECT ANSWERS
We analyzed the decoding dynamics of incorrect samples. We conducted an additional analy- sis tracking the "Last Change Step" for samples that eventually resulted in incorrect answers on GSM8K. As illustrated Figure 5 updates for incorrect answers are heavily right-skewed (oceur- ring in the last 20% of steps). This indicates that when the model produces incorrect answer, it is typically uncertain rather than overconfidently wrong. continues to fluctuate and flip its prediction until the very end of the generation process. This behavior serves natural safety mechanism for Prophet. Since incorrect answers rarely exhibit early stability or high confidence, they fail to trigger the "Early Commit" criteria. Consequently, Prophet correctly forces the model to utilize the full step budget for these difficult cases, rather than exiting prematurely. This explains why our method achieves significant speedups (by catching the left-skewed correct answers) without compromising accuracy (by avoiding the unstable incorrect ones).
Number Samples
40
20
80
100
Last Final Answer Change
(% Total Decoding Steps)
Figure 5: Distribution of the "Last Change Step" for incorrect answers. The right-skewed distribu- tion ensures Prophet acts conservatively on uncertain samples.
C IMPLEMENTATION DETAILS
C.I ALGORITIM DESIGN
Suffix prompt mechanism. The suffix prompt is inserted as a semantic anchor near the end of the generation window, followed by mask tokens reserved for the final answer. The resulting quence structure is: [Question] [MASKs for Reasoning Chain] The answer is [MASKs for Final Result]. This mechanism accelerates convergence because diffusion
17

---

Published as a conference paper at ICLR 2026
models generate bidirectionally. Without an explicit cue, the model spends early denoising steps implicitly determining output format. The suffix prompt eliminates this ambiguity by conditioning the model to expect solution in the designated region, allowing answer tokens to stabilize sig- nificantly earlier and enabling Prophet to trigger early exit sooner, as illustrated in Figure and Figure
We note that utilizing a suffix prompt does not constitute the use of oracle information. The answer region position is deterministically assigned by the input prompt rather than derived from ground- truth labels. We instruct the model where to place the answer, not what the answer is. Both Prophet and the baseline use identical suffix prompt configurations: the performance gains therefore stem from convergence detection rather than any structural advantage.
Answer region determination. The answer region is defined based on the structural properties of each task:
Mathematical reasoning (e.g., GSM8K); The dataset provides a standard separator between the reasoning chain and the final result. We define the answer region as the tokens following this separator.
General reasoning (e.g., MMLU, ARC-C): These benchmarks consist of multiple-choice ques- tions. The suffix prompt explicitly locates the position for the final prediction (e.g.. "A" or "B"), and Prophet monitors the confidence gap this single token.
Code generation (e.g., HumanEval): The answer region defined as the generated function body, delimited by a pre-inserted code block separator (e.g., ' >>) The strict syntactic dependencies of code allow the model to converge on the correct program structure early, enabling safe acceleration without breaking syntax.
Regarding answer region length, we acknowledge that this specific setting relies on task priors. For example, in GSM8K, the answer is typically concise number, whereas more complex mathemati- cal problems (like AIME) might require variable-length outputs. However, this is not fundamental limitation; in more complex scenarios, the fixed window can be replaced by dynamic semantic ex- traction techniques (similar to Wang et al. (2025a)) to identify the answer span on the fly. We opted for pre-defined region primarily for implementation simplicity and conciseness. Ultimately, our objective is to reveal and rigorously analyze the intrinsic phenomenon of Early Answer Con- vergence, not to compete with engineering-oriente SOTA acceleration frameworks by building a universally applicable tool.
C.2 EVALUATION AND HYPERPARAMETER
We re-implemented the evaluation of LLADA and DREAM on all reported datasets. Following standard practice, we generate and extract the final answer rather than comparing log probabilities in multiple-choice settings, which can slightly lower scores on some benchmarks when the model fails to produce a response in the expected format. Full experimental configurations are summarized in T
Confidence schedule design. Early commitment is governed by the three-stage confidence schedule defined in Eq. 3 designed around a principle of time-varying risk aversion. In early denoising steps, predictions are noisy, so we enforce high threshold Thigh to prevent premature commitment. As decoding progresses and the marginal gain of further computation decreases, we progressively lower the threshold to encourage earlier exit.
Hyperparameter robustness. We did not perform an extensive hyperparameter search. A light sweep on small GSMSK validation set revealed that three uniform stages with the thresholds in Table 6 work consistently well across all tasks. The robustness of this fixed configuration suggests that the confidence gap is a model-agnostic metric that generalizes without sensitive per-task tuning.
We chose staged schedule for its simplicity and ease of implementation. To verify that our results are not contingent on this specific functional form, we conduct an ablation comparing the staged schedule against a continuous linear decay 7(p) 8.0 4.5 p covering the same threshold range [8.0, 3.5). As shown Table 7 the two schedules yield comparable accuracy and speedup. confirming that the observed gains stem from the inherent early convergence property of the model rather than from a carefully engineered controller. Continuous or learnable schedules remain promising direction for future optimization.
18

---

Published as a conference paper at ICLR 2026
Table 6: Configurations used in our runs. We keep only parameters relevant to our method: base budget (L,T.B) and PROPHET's confidence schedule defined Eq.
Benchmark
Base Budget (L, B)
PROPhET Thresholds (Thigh Tiidd Tlo
Transition Points
MMLU
64 T464, B=16
(7.5, 5.0, 2.5)
33%, 67%
ARC-C
L-64, T=64, B=16
(7.5, 5.0, 2.5)
33%, 67%
Hellaswag
I4, T=61, B=16
(7.5, 5.0, 2.5)
33%, 67%
TruthfulQA
L464, T=64, B=16
(7.5, 5.0, 2.5)
33%, 67%
WinoGrande
I-44, T=64, B=16
(7.5, 5.0, 2.5)
33%, 67%
PIQA
141 T=61, B=16
(7.5, 5.0, 2.5)
33%, 67%
GSMSK
256 T-256, B=32
(8.0, 5.0, 3.5)
33%, 67%
GPQA
156 T=256, B=32
(8.0, 5.0, 3.5)
33%, 67%
HumanEval
L=512, T=512, B=32
(7.5, 5.0, 4.5)
33%. 67%
MBPP
L=512, T=512, B=32
(7.5, 5.0, 4.5)
33%, 67%
Sudoku
L-24, T-24, B-24
(7.5, 5.0, 2.5)
33%, 67%
Countdown
I.=32, T=32, B=32
(7-5, 5.0, 2.5)
33%, 67%
Table 7: Comparison of threshold schedules on GSM8K (LLaDA-8B).
Schedule Type
Accuracy (%)
Avg Steps
Staged (Ours)
77.9
160
Linear (Ablation)
77.4
154
19


Reviews:
[{"review_id": "RJD2ctQaBs", "reviewer": "Reviewer_rmQd", "summary": "The authors propose a novel method to accelerate sampling in diffusion language models. In particular, they focus on the observation that answers often converge in intermediate steps. To this end, they introduce a method that enables up to 3.4x reduction of the sampling steps training-free. The efficacy of the method is demonstrated in two models and multiple benchmarks.", "questions": "I would appreciate it if the authors could address the issues and questions raised in the weaknesses section.", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The main idea is novel and clearly presented.\n\n- The studied problem is relevant and the proposed solution appropriately motivated.\n\n- The empirical evidence regarding convergence in intermediate steps is valuable for the community.\n\n- The experimental results showcase that the proposed methods can achieve significant speed-ups with similar performance.\n\n- I appreciate the thorough ablations.", "weaknesses": "- The only major weakness I see is the lack of comparison with other methods that aim to reduce the number of steps, e.g., distillation-based. This would help put the latency-reduction in perspective.\n\n- Could the decision to terminate a decoding loop be learnable, for example by a judge network, e.g., similar to [1]? A discussion on this would be interesting and potentially fruitful for future directions.\n\n- I am missing some related works that aim to reduce the number of sampling steps, e.g., [2].\n\n[1]. Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment\n\n[2]. Beyond Autoregression: Fast LLMs via Self-Distillation Through Time", "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": "Qapnh151Fk", "reviewer": "Reviewer_wXCe", "summary": "This paper investigates sequence generation with discrete diffusion models and makes an interesting observation: in many cases, it is possible to decode the correct answer early in the denoising process such as 25%-50% of the total steps. Motivated by this observation, the authors propose a method to monitor answer convergence and propose to track the difference between the top1 and top2 logits of the answer tokens. If that difference exceeds a threshold (dependent on the step t) then the complete answer is decoded in a single step. The authors evaluate this protocol on a variety of benchmarks and find that performance degradation is minimal, while at the same time speedups are very high.", "questions": "1. I was a bit surprised that you observed even some gains on some benchmarks when applying your early-decoding strategy. Do you have any intuition regarding why this happens? Showing such an example where correct tokens are later changed would be interesting.\n2. I could imagine that looking at top1 and top2 logits per position can also bring some issues? E.g. in coding, the model might be unsure whether to call a given variable “x” or “y”, keeping the difference quite small although both immediate decodings might be fine. Do you observe any such behavior, where even earlier decoding would have worked but “Prophet” was kind of stalled due to such ambiguous tokens in the answer?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The idea is very well-motivated with an initial exploration on GSM8K and MMLU, which makes it easy for the reader to follow the thought-process of the authors and also appreciate the idea of answer convergence. \n2. The proposed algorithm is really simple, does not need any training and can be used for any discrete diffusion model. The speedups are quite strong and the evaluation suite is quite broad, so this technique does seem to work on most benchmarks of interest.", "weaknesses": "1. The formatting and writing of the paper can be improved, some things seem imprecise in the main text, e.g. how is $\\hat{g}_t$ exactly defined? Section 4 seems to suggest that it is an average over all positions, but Algorithm 1 seems to suggest you only average over the answer tokens? The latter seems to make more sense to me but it would be great if you could clarify this in the main text. In general, the nature of answer tokens is not super clear to me; for GSM8K the definition seems pretty straightforward but for problems like HumanEval, where the answer comprises a piece of code, the definition is a bit less clear. Things like “Answer length” and “Block length” should also be defined better so that the reader can more easily follow.\n2. There seem to be quite some hyper parameters for the method, i.e. a schedule needs to be set which right now is somewhat arbitrarily divided into three uniform sections, and then each section needs a corresponding threshold. How exactly did you determine these values? You mention some validation experiments but I couldn’t find much more detail in the paper. Does setting those parameters on one benchmark transfer to another, or does the validation data need to be diverse enough?", "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": "qvqAV5kTw6", "reviewer": "Reviewer_ETka", "summary": "This paper aims to address a key challenge in Diffusion Language Models (DLMs): although DLMs inherently offer the potential for parallel decoding, their inference speed in practice often lags behind that of autoregressive (AR) models.\nThe core contribution of the paper lies in identifying and leveraging an overlooked property, Early Answer Convergence. Through extensive experiments, the authors demonstrate that in many cases, DLMs internally determine the correct answer well before the decoding process concludes, often around halfway through the total number of steps.\nBuilding on this insight, the authors propose Prophet, a training-free fast decoding paradigm. Prophet introduces an Early Commit Decoding strategy that dynamically monitors the model’s confidence in its predictions at each decoding step. Specifically, it measures the Confidence Gap between the top-1 and top-2 predicted candidates and employs an adaptive threshold that evolves with decoding progress to decide when to go all-in.\nExperimental results show that Prophet achieves substantial decoding acceleration—up to 3.4× speedup—while maintaining, and in some tasks even slightly improving, the generation quality.", "questions": "Included in Weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The observation of early answer convergence is novel and interesting.\n2. The proposed method is lightweight and requires no additional training.\n3. It demonstrates a significant acceleration in inference.", "weaknesses": "1. The paper appears to heavily rely on the suffix prompt “Answer:”, yet the implementation details are not provided. It is unclear whether this suffix is appended to the end of the question or to the generated sequence.\n2. I am also puzzled by why adding the suffix prompt “Answer:” leads to such a significant difference in Figures 1 and 2. The authors should provide a deeper explanation or interpretation of this phenomenon.\n3. The paper does not seem to explain how the answer region positions are determined. Moreover, for general question-answering tasks beyond math problems, it is possible that the entire generated sequence could be considered as answer region positions.\n4. Although the “go all-in” strategy appears to maintain accuracy on the “answer,” it is unclear whether it might compromise aspects such as the correctness of intermediate reasoning steps, grammatical accuracy, or overall fluency.\n5. The paper should provide some complete, real cases to help evaluate and analyze the decoding behavior and dynamics of the proposed method.\n6. The notion of “L” appears to be used inconsistently.\n7. This appears to be a rapidly evolving field. In the past few months, several works have emerged on accelerating DLM inference by reducing sampling steps (e.g., arXiv:2505.22618, arXiv:2506.10848, arXiv:2507.18578). Some of these methods seem to achieve even more pronounced acceleration effects compared to this paper. A more detailed comparison and discussion with these contemporaneous approaches would be very valuable. (I am not certain about ICLR’s policy regarding “concurrent works.” If such comparisons are deemed unnecessary under that policy, the authors may clarify and omit this comment accordingly.)", "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": "ORTTir4Pw2", "reviewer": "Reviewer_5Tqf", "summary": "The paper presents a new decoding algorithm, Prophet, for diffusion language  models that enables early stopping of refinement using a dynamic schedule based on token logits. The paper provides empirical observation that motivates the methodology: In a lot of instances the correct answer is already decoded halfway through the decoding steps, and the remaining steps are only additional refinements on the reasoning. The paper evaluates their decoding algorithm and shows that early stopping provides speedup without significant drop in accuracy.", "questions": "- To clarify, is the experimental evaluation in section 5 using suffix prompting? If not, what does the performance looks like for Prophet vs baseline? It seems to me from the early convergence analysis that suffix prompting and Prophet should synergize?\n- What are the percentages of the step budget used by Prophet in the benchmarks? Does the distribution of used steps correspond to the early convergence analysis?\n- Why use a staged threshold function? Why not make $\\tau(p)$ a continuous function? What does the performance look like if it is simply a linear function?\n\n- The early answer convergence analysis only shows the histograms for correct answer. What does the distribution look like for incorrect answer? For all answers? I am curious to see if the distribution for incorrect answers skews right (implying the model is simply uncertain of the answer) or skews left (implying the model is overconfidently wrong).\n- In Figure 2, how come there are some orange marks to the right of the blue marks? Is it that the token is changed and then rechanged back?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The early stopping idea is simple but effective. The presented decoding algorithm clearly presents performance benefits and is relatively simple to implement and deploy. There does not seem to be any downsides to default to using this algorithm.\n- The algorithm works for different generation lengths, re-masking strategies, and even improves course-grained block refinements\n- The paper is well-written; the empirical observation and resulting algorithm are described clearly.", "weaknesses": "- While the presented experimental evaluation in section 5 is fair and sound, the work would benefit from more ablations to fully explore the limits (or capability) of the methodology. See Questions for what experiments should be added.\n- The paper is well written in general, but Section 5 seems to have some typos that makes the section a bit hard to read. Namely, the text refers to \"three decoding strategies\" but only show full baseline and prophet. Section 5.2 also refer to a \"half baseline\" that is undefined and not shown in Table 1.\n- The table column headers should be made clearer. In table 1, it was not immediately clear that columns 2,3,5,6 were showing accuracy percentages and the orange is the speedup. The column header should clearly label \"accuracy\" and have the Prophet speedup as its own column with label \"Speedup\". In table 2, It was also confusing what 16, 32, 64, 128 refer to at first. There should be a header label that says \"step budget\"", "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 (Oral)