Fast-dLLM v2: Efficient Block-Diffusion LLM
Authors: Chengyue Wu
Hao Zhang
Shuchen Xue
Shizhe Diao
Yonggan Fu
Zhijian Liu
Pavlo Molchanov
Ping Luo
Song Han
Enze Xie
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion LLM
Efficient AI
Primary area: foundation or frontier models, including LLMs
TL;DR: Fast-dLLM v2 transforms pretrained autoregressive LLMs into efficient block diffusion models, matching accuracy while delivering up to 2.5× faster decoding with minimal data and training cost.

Abstract: Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent sequential decoding limits inference efficiency. In this work, we propose Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that efficiently adapts pretrained AR models into dLLMs for parallel text generation—requiring only ∼1B tokens of fine-tuning. This represents a 500× reduction in training data compared to full-attention diffusion LLMs such as Dream (580B tokens), while preserving the original model’s performance. Our approach introduces a novel training recipe that combines a block diffusion mechanism with a complementary attention mask, enabling blockwise bidirectional context modeling without sacrificing AR training objectives. To further accelerate decoding, we design a hierarchical caching mechanism: a block-level cache that stores historical context representations across blocks, and a sub-block cache that enables efficient parallel generation within partially decoded blocks. Coupled with our parallel decoding pipeline, Fast-dLLM v2 achieves up to 2.5× speedup over standard AR decoding without compromising generation quality. Extensive experiments across diverse benchmarks demonstrate that Fast-dLLM v2 matches or surpasses AR baselines in accuracy, while delivering state-of-the-art efficiency among dLLMs—marking a significant step toward the practical deployment of fast and accurate LLMs.

Published as a conference paper at ICLR 2026
FAST-DLLM V2: EFFICIENT BLOCK-DIFFUSION LLM
Chengyue Wu Hao Zhang?
Shuchen Xue
Shizhe Diao²
Yonggan Fu² Zhijian Liu?
Pavlo Molchanov Ping Luo
Song Han
Enze Xie²
'The University of Hong Kong NVVIDA MMIT
https: /ttp::///vllbaae github. httpe/ii//aaat//////...
ABSTRACT
Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent se- quential decoding limits inference efficiency. this work, we propose Fast-dLLMM v2, a carefully designed block diffusion language model (dLLM) that efficiently adapts pretrained AR models into dLLMs for parallel text generation- only ~IB tokens of fine-tuning, This represents 500x reduction in training data compared to full-attention diffusion LLMs such as Dream (580B tokens), while preserving the original model's performance. Our approach introduces novel training recipe that combines block diffusion mechanism with a complemen- tary attention mask, enabling blockwise bidirectional context modeling without sacrificing AR training objectives. To further accelerate decoding, we design a hierarchical caching mechanism: a block-level cache that stores historical context representations across blocks, and a sub-block cache that enables efficient parallel generation within partially decoded blocks. Coupled with our parallel decoding pipeline, Fast-dLLM v2 achieves to 2.5x speedup over standard AR decoding without compromising generation quality. Extensive experiments across diverse benchmarks demonstrate that Fast-dI.LM v2 matches or surpasses AR baselines in accuracy, while delivering state-of-the efficiency among dl...Msmmrring significant step toward the practical deployment of fast and accurate LLMs.
INTRODUCTION
Qwen2.55
Fast-dLLM
250
Qne 5-70- intruct
GSMBK Accuracy (96)
+5.2%
Fast-SLLM v2(76)
Throug hput (taleasss)
20
LLaDA(SB)
15D
LLaMA- 3-88
Fast-ELLM-LLaDA/0B)
Dream(78)
102.5
50-
48.2
30.
70
20
40
60
80
100
Throughput (Tokens/sec)
(b)
Figure 1: Performance comparison of Fast-dLLM v2. (a) Comparison of throughput and GSM8K accuracy among baseline models and the Fast-dLLMM variants in A100. Fast-dLLMM v2 (7B) achieves 2.54x higher throughput than Qwen2.5-7BB-nnstte
while offering comparable accuracy. Addition- ally. improves accuracy by +5.2% over LM-LLLL.M-LLLDA, which is based on optimized LLaDA. (b) Throughput comparison under different batch sizes. Fast-dLLM v2 significantly outperforms all baselines both batch size 1 and demonstrating superior scalability and efficiency.
Recent years have witnessed autoregressive (AR) large language models (LLMs) (Radford & Narasimhan, 2018; Radford al., 2019; Brown al., 2020; OpenAI, 2022) achieving remarkable

---

Published as a conference paper at ICLR 2026
performance across a wide range of natural language tasks. Their capacity to generate fluent, coher- ent text by modeling next-token prediction has made them the prevailing paradigm in most deployed systems. However, AR models suffer from inherent inefficiencies: Since tokens generated one- by-one in a strict left-to-right order, they cannot exploit full parallelism during decoding.
On the other hand, diffusion- based language models (dLLMs) (Google DeepMind, 2025; Inception Labs, 2025; Zhu et al., 2025; Ye et al., 2025a) offer a promising alternative. By allowing multiple tokens (or even entire blocks of tokens) be predicted or refined jointl, dLLMs can in principle achieve much higher decoding parallelism. Nevertheless, in practice, they come with their own significant drawbacks: they often cannot use KV cache effectively due to bidirectional attention, their inference latency often exceeds that of AR models, and many require fixed sequence lengths or have restricted flexibility in generation length. These limitations have prevented diffusion-bart -based models from outperforming AR models in speed while maintaining comparable quality. Some works employ approximate KV cache mechanisms to reuse computation, such as the DualCache in Fast- dLLM (Wu et al., 2025). However, this does not fundamentally resolve incompatibility of dLLMs with KV cache, since such approximate caches are not equivalent to the original computation.
To bridge these paradigms, block diffusion language model (as in BD3-LMs (Arriola et al., 2025)) has been proposed: it interpolates between purely autoregressive and diffusion regimes by gen- erating tokens in blocks, performing diffusion within each block, while conditioning on previous blocks autoregressively. BD-LMM achieve two desirable properties: flexible sequence length (arbi- trary or variable length generation), and KV caching between blocks, enabling improved inference efficiency. However, BD3-LMs have so far only been validated on relatively small-scale models and conventional LM metrics, rather than modern large-scale LLM settings. As such, their practical ap- plicability to state-of-the-art LLMs remains unclear, especially in terms of maintaining high-quality text generation and robust scaling behavior.
In this work, we propose Fast-dLLM v2, carefully designed block diffusion language model (dLLM) that transforms pretrained autoregressive (AR) models into diffusion-style decoders for parallel text generation. Unlike prior block diffusion approaches that remain limited to small-scale validation, L v2 explicitly built to scale to large LLMs and real-world tasks. A key fea- ture of Fast-dLLM v2 is its data efficiency: while full-attention diffusion models such as Dream (Ye et al., 2025a) require on the order of 500B tokens for fine-tuning, our method adapts AR models into block diffusion models with only about IB tokens of fine-tuning- achieving lossless adapta- tion without retraining from scratch. Unlike the full-attention dLLM in Dream, our design uses a block-wise attention mask structure closer to the original AR models, making the adaptation pro- cess inherently more compatible and data-efficient. Our method further introduces a novel training recipe that combines a block diffusion mechanism with a complementary attention mask, enabling biock-wise bidirectional context modeling while simultancously preserving the original AR train- ing objectives and predictive performance. To enhance inference speed, we design hierarchical caching mechanism: block-level cache that stores historical context representations across blocks, and a sub-block cache that supports efficient parallel decoding within partially generated blocks which adopts the DualCache in Fast-dLLM (Wu et al., 2025).
In consequence, Fast-dLLLM v2 achieves up to 2.5x speedup over standard AR decoding without compromising generation quality. Extensive experiments across diverse tasks confirm that Fast- dLLM v2 not only matches the accuracy of AR baselines but also achieves state-of-the-e efficiency among diffusion- LLMs, marking a significant step toward the practical deployment of fast and accurate language models. In summary, our contributions are threefold:
1. We identify the AR-frienlly nature of our block-wise attention design and leverage it to present post-training strategy for adapting pretrained AR models into block-diffusion frameworks, requiring only affordable fine-tuning rather than full retraining. Specifically, Fast-dLLM v2 achieves lossless adaptation with just IB tokens, compared to 550B tokens required by Dream (Ye et al., 2025a).
2. We introduce an inference strategy that combines a hierarchical caching mechanism with block-wise parallel decoding. This design enables effective reuse of context across blocks and accelerates token generation within each block, yielding substantially faster inference than prior diffusion-based methods.
2

---

Published as a conference paper at ICLR 2026
3. We conduct comprehensive large-scale experiments models up to 7B parameters and diverse tasks, showing that Fast-dLLM v2 achieves up to 2.5x speedup over standard AR decoding while maintaining comparable generation quality.
RELATED WORK
2.1 MASKED DIFFUSION LLM
Initial work by (Sohl-Dickstein al., 2015; Hoogehoom al., 2021) first pioneered the of diffusion models for discrete data. This concept was later generalized by D3PM (Austin et al., 2021) using forward process defined as a discrete-state Markov chain with general transition matrices Qe- CTMC (Campbell et al., 2022) extended this to continuous time, while SEDD (Lou et al., 2023) instead modeled the likelihood ratio Re(M) using Denoising Score Entropy. Masked Diffusion
(.)
Models (MDMs) also called absorbing state discrete diffusion--re prominent in discrete diffusion models. During training, MDMs randomly replace tokens with special [MASK] token according to mask ratio t, where & E [0, 1] interpolates between zy 0) and fully masked sequence (t = 1). The MDMs have been scaled up to 7B level, with LLaDA (Nie et al., 2025) being trained from scratch on the MDM loss, and Dream (Ye et al., 2025b;a) being adapted from the existing Qwen-2.5 7B (Qwen et al., 2025).
2.2 INTERPOLATION BETWEEN AUTOREGRESSIVE AND MASKED DIFFUSION
Several recent works have explored block-wise diffusion for non-autoregreesive text generation. SSD-LM (Han et al., 2022) introduced a block formulation of Gaussian text diffusion. Building on this, AR-Diffusion (Wu al., 2023) extended SSD-LM by incorporating left-to-right noise schedule. For masked diffusion models, BD3-LM (Arriola et al., 2025) interpolates between dis- crete denoising diffusion and autoregressive models by using inner-block diffusion within global left-to-right structure. Concurrent to our work, SDAR (Cheng et al., 2025) successfully finetuned block diffusion model from pretrained autoregressive model. While sharing the conceptual di- rection of AR-to- RR-to-diffusion adaptation, our work diverges by establishing a systematic framework designed for high data efficiency and decoding robustness. Specifically, we introduce sub-block de- coding to resolve the performance degradation caused by training-inferenee inference block size mismatches, and employ complementary masking to maximize the denoising signal within blocks. D2F (Wang et al., 2025b), inspired by Diffusion Forcing (Chen et al., 2024) and Caus Vid (Yin et al., 2025), distilled large diffusion language model (dLLM) into a more efficient block diffusion model. Set Block Decoding (Gat et al., 2025) integrates standard next-token prediction (NTP) and masked token prediction (MATP) within a single architecture to enable the generation of multiple tokens simulta- neously. Compared to these concurrent works, Fast-dLLM v2 is distinguished by its data-efficient fine-tuning process, requiring only 1B tokens.
2.3 ACCELERATION FOR DIFFUSION LLM
Recent research has focused on accelerating the inference of diffusion language models, primar- ily through two avenues: caching mechanisms and advanced decoding strategies. significant bottleneck in dLLM inference is the computational cost associated with the bidirectional attention mechanism. To address this, several caching techniques have been proposed. FAST-DLLM (Wu et al., 2025) proposed DualCache. This method caches the KV activations for both the preceding text (prefix) and the subsequent masked tokens (suffix). dKV-Cache (Ma et al., 2025) proposed a de- layed caching strategy. M-CaMe (Liu et al., 2025) accelerates inference by combining prompt caching with an adaptive partial response cache that decides whether to reuse or recompute the generated prefix each step to balance efficciecy and accuracy. Sparse-dLLM (Song et al., 2025) accelerates inference by using the model's attention scores to dynamically drop unimportant tokens from the Value cache. DPad (Chen al., 2025) proposed to restrict the attention mechanism to a small, fixed-size window of recent suffix tokens.
As for advanced decoding strategies, FAST-DLLM (Wu al., 2025) proposed adaptive confidence- based parallel decoding algorithm. EB-Sampler (Ben-Hamn et al., 2025) introduces a simple drop-in replacement for existing samplers, utilizing an Entropy Bounded unmasking proce-
3

---

Published as a conference paper at ICLR 2026
Block
Block I
Block
Past
ILLLM
senes
SErRES
of
diffisten
barge
lmnginge
models
Mask
Transformer Decader with Block-wise Cousal Attention
Non-Mask token
Predicted token
The
[Masi]]
dLEM
[Mask]
[Mask]
largo
[Mask]
models
GT takes- Mask token correspandenes
Complementary masks
MMak]
[Misk]
[Mask]
drisin
[Mask]
[Misk]
Figure 2: Training process of - The input sequence is decoded block by block. Within each block, the model performs next-token prediction with partial masking. To ensure every token trained, complementary masks introduced so that masked tokens in view be predicted from the other. We only apply loss to predicted tokens that are highlighted in green, and dashed curves connect Mask tokens to their corresponding predictions.
dure that dynamically unmasks multiple tokens in one function evaluation with predefined approx- imate error tolerance. Dimple (Yu et al., 2025) proposed confident decoding, which dynamically adjusts the number of tokens generated each step. WINO (Hong al., 2025) employs a parallel draft-and-verify mechanism, aggressively drafting multiple tokens while simultaneously using the model's 's bidirectional context to verify and re-mask suspicious ones for refinement. SlowFast Sam- pling (Wei et al., 2025) proposed a dynamic sampling strategy that adaptively alternates between exploratory and accelerated decoding stages. LaViDa (Li et al., 2025c) tests timestep shift for ef- ficient sampling. Wang et al. (2025a) proposed Temporal Self-Consistency Voting that aggregates predictions across denoising steps to select the most consistent output. Prophet (Li et al., 2025b) dynamically decides whether to continue refinement decode all remaining tokens in step.
Beyond accelerating diffusion models themselves, block diffusion is also compatible with Auto- Regressive (AR) acceleration techniques, such speculative decoding. DiffuSpec (Li et al., 2025a) demonstrates that a small diffusion model (e.g., Dream-7B can serve as an effective draft model for speculative decoding, achieving 3.1 speedup with AR models like Qwen2.5-32B. Given that FAST-DLLM v2-7B is nearly 10x faster than Dream-7B (as shown Figure 1a), employing as draft model within similar frameworks offers promising avenue further enhance decoding efficiency in future work.
3 METHODOLOGY
3.1 PRELIMINARY
Let (x=1 x2" x.. denote token sequence of length L. Traditional autoregressive models generate sequentially by modeling the conditional distribution (o(x*12<*) and are trained to minimize the cross-entropy loss.
In contrast, diffusion language models define generative distribution via a forward noising process and learned reverse denoising model. At time t (0, 1), each token in æn is masked independently with probability i, producing corrupted sequence dt- The reverse model po (o(oo))))) predicts the original tokens given the noised input.
The model is trained to minimize the expected masked token prediction loss:
}
[())- = -Ee,zo
14 [MASK]] log log po
xx))
1=1
where Uniform(0, 1) and x, is sampled from the forward process. The loss is computed only over the masked tokens.
3.2 ADAPTATION TO BLOCK DIFFUSION LLM
We build our block-wise diffusion training pipeline on top of pretrained Qwen2.5- Instrrcct mod- els (Qwen et al., 2025), including both 1.5B and 7B variants. Fine-tuning is conducted as super-
4

---

Published as a conference paper at ICLR 2026
Decoded Blocks
The Decuding Block
The Decoding Block
block
block
Cached
Transformet Decoder with Block-wise Causel Attention
Zoom
Transformer Drowder with Block-iist Crasal Amortion
Mask tnken
Decode taken
Figure 3: Illustration of the inference process. The sequence is decoded block- borkk----bockk The de- coded blocks are cached to speed up inference. Within each block, adopt the parallel decoding and DualCache in fast-dLLM to further accelerate inference.
vised fine-tuning (SFT) instruction-tuning data, where each training batch is constructed using our blockwise diffusion setup. Specifically, we introduce partial token masking within each block together with complementary masking strategy (Li et al., 2025c) to ensure that every token is trained in both visible and masked contexts. The overall architecture and training workflow are illustrated in Figure 2.
Block- wise organization. Given set of tokenized samples, we first pad each sequence to a length that an integer multiple the block size D by appending [MASK] tokens as needed. These padding tokens are ignored in the loss computation and do not contribute gradient updates. After this alignment step, we pack the padded sequences by concatenating them into a long token stream and splitting it into training sequences of fixed context length L. Each packed sequence is therefore naturally divided into B = L/ non-overlapping blocks of size D. already aligned by construction. This block-aligned packing ensures efficient batching while avoiding block boundaries crossing sample boundaries.
Masked token prediction with complementary views. For each block, we randomly sample a binary mask m E {0, 13 where m, = means position j is replaced with learned [MASK] embedding. To ensure all tokens receive both masked and unmasked supervision across training, complementary masking strategy: each training sample is duplicated into views with masks m and m = m. These two views are placed together in the same batch, so the model can jointly see masked and unmasked contexts across the views.
Token shift for prediction. To preserve the pretrained AR model's representation quality (Ye et al., 2025b;a), we adopt a shifted-label strategy: prediction of token at masked position uses the logit from its preceding position (i - 1). Concretely, if x; is masked, the model uses the hidden state at i 1 to predict Tú, consistent with the next-token prediction mechanism in causal language models. This allows dLLM maintain AR-like temporal representations while supporting intra- block diffusion.
Training objective. We minimize the masked-token-only cross-entropy loss:
Latock (0) -B
Σ 1
[MASK]]
po
| (Block()))
where Tblock(s) denotes all tokens in the block containing position i (including masked/unmasked), and Fzz are clean tokens from earlier blocks.
Bisek-wise attention masking. We use hybrid attention scheme similar to that in (Arriola al., 2025). For each training sample, concatenate the noised sequence 2; and its corresponding clean sequence Zn along the sequence dimension, resulting in total length 2L. The attention mask A E {0, 1]22X2L is then applied control both causal and bidirectional connections.
This design allows simultaneous processing of corrupted and clean contexts, facilitating comple- mentary mask supervision. The attention mask naturally supports both block-parallelism and causal autoregressive dependencies between blocks. We further employ the flex-attentiion implementation to efficiently realize this structured masking and accelerate training.
5

---

Published as a conference paper at ICLR 2026
3.3 INFERENCE PIPELINE
At inference time, Fast-dLLM v2 employs a block-wise decoding strategy that balances the autore- gressive nature of LLMs with the parallelism afforded by iuffusion-based decoding. As shown in Figure 3, generation proceeds one block at time: previously decoded blocks are cached and reused as clean prefix context, while the current block undergoes parallel masked token refinement.
By combining block-level caching, intra-block parallel decoding, and DualCache reuse, Fast-dLLM v2 maximizes decoding efficiency without requiring auxiliary models or extra inference overhead. Empirically. we observe up to 2.5x speedup compared to standard AR decoding, while maintain- ing generation quality on par with strong autoregressive baselines. This makes Fast-dLLM v2 a compelling candidate for practical LLM deployment in latency-sensitive applications.
Block-wise autoregressive decoding with caching. Since each block in Fast-dLLM v2 is decoded in causal order, we naturally preserve left-to-right semantics across blocks. After decoding each block, its unmasked tokens are cached as read-only context for future blocks. This design enables block-level Key-Value (KV) cache reuse and significantly reduces redundant computation. The attention mask at inference time allows each block to attend bidirectionally within itself, while attending causally to preceding blocks, mirroring the configuration used during training.
Parallel refinement within each block. To accelerate generation within block, we adopt the confidence-awarc parallel decoding strategy proposed in Fast-dLLM (Wu et al., 2025). Specifi- cally, we iteratively refine masked tokens in the current block based on their model confidence of predicted tokens. Tokens exceeding a confidence threshold are decoded and unmasked in parallel, while uncertain positions remain masked for future refinement. This avoids ambiguous predictions and reduces generation latency.
DualCache for sub-block reuse. To further reduce redundant computation during intra-block de- coding, we integrate the DualCache mechanism from Fast-dLLM. DualCache maintains both prefix and suffix KV caches for partially decoded blocks, enabling efficient recomputation as additional tokens are revealed. This hierarchical caching not only rules out expensive recomputation, but also supports the iterative, selective decoding pattern used in confidence-awarc refinement.
Batch decoding with padding. To support batch generation of sequences with varying target lengths, we right-pad each sequence with [MASK] tokens to make their total lengths divisible by the block size D. The sequences are then grouped and decoded block-by-block. At each step, all sequences in the batch decode the next block in parallel, regardless of how many real tokens remain, ensuring consistent and efficient scheduling on modern hardware.
4
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We conduct adaptation experiments the Qwen-2.5 1.5B and 7B Instruct models, tuning them into the Block Diffusion LLM configuration. For training, we use the LLaMA-Nemotron post-training dataset (Bercovich al., 2025) with batch size of 256. The learning rate and training steps are set specifically for each model: the 1.5B model is trained with a learning rate of 2 x 10-5 for 6,000 NVIDIA
steps, while the 7B model uses learning rate of 10 - for 2,500 steps. We employ A A100 GPUs for training, with the 1.5B model training for approximately 8 hours and the 7B model for 12 hours. Unless otherwise stated, the sub-blocd size fixed at 8, block size 32, and parallel decoding is disabled (i.e., threshold is set 1).
For benchmarking, we evaluate the tuned models on a comprehensive suite tasks, covering diverse aspects of language modeling and reasoning abilities. The evaluation suite includes code genera- tion tasks like HumanEval and MBPP, mathematical reasoning tasks such as GSM8K and MATH, well knowledge-intenn intensive benchmarks like MMLU and GPQA, instruction-followii such as IFEval. Code-related benchmarks, including HumanEval and MBPP, are assessed using the EvalPlus framework, which provides a robust evaluation for code synthesis. All other benchmarks
tasks

---

Published as a conference paper at ICLR 2026
Table 1: Benchmark results of various language models across range of evaluation tasks. Mod- els are grouped by parameter scale into 1B and 7B+ categories. Evaluation metrics include code generation (HumanEval, MBPP), mathematical reasoning (GSM8K, MATH), instruction following (IFEval), knowledge-intensive QA (MMLU, GPQA), and general average score (Avg.). "Base" and "Plus" refer to different evaluation settings code benchmarks using EvalPlus. The best results column are in bold, and the second-best are underlined.
EvaanEval
MBPP
Model
#Params
GSM8K
Math
IFEval
MMLU
GPQA
Avg.
Base
Plus
Base
Plus
Models
LlaMA-322
34.1
34.1
29.4
43.0
23.8
58.9
44.4
24.1
35.9
SmolLM 2
1.7B
34.1
28.7
50.6
46.0
47.7
21.1
55.1
49.1
29.2
40.7
Qwen2.5-14
1.58
42.1
37.2
48.1
41.3
57.0
46.8
41.2
54.6
30.6
44.3
Owen2.5-1.B-NNemo-F
1.5B
37.2
33.5
53.4
44.4
58.5
43,5
39.4
58.1
31.0
44.3
Fast-dI.LM v2
1.5B
43,9
40.2
50.0
41.3
62.0
38.1
47.0
55.1
27.7
45.0
Models
LLaDA
8B
35.4
31.7
31.5
28.6
78.6
26.6
59.9
65.5
31.8
43.3
LL
8B
52.4
42.8
83.3
42.6
58.2
66.0
36,9
LLaDA-MoE
7B
61.6
70.0
82.4
58.7
59.3
67.2
Dreum
7B
57.9
68.3
56.
81.0
39.2
62.5
67.0
33.0
57.6
Qwen2.5-7B
51.2
57.7
71.4
73.3
70.8
68.7
58.2
Qwen2.5-7B 7-on25-7B-Nemo-FF
7B
52.4
48.2
57.1
50.0
84.1
72.0
69.5
68.6
34.2
59.6
Fast-dLLM v2
7B
63.4
58.5
63.0
52.3
83.7
61.6
61.4
66.6
31.9
60.3
are evaluated using the LM--Eal framework, ensuring consistency and reliability of performance measurements across different tasks.
To provide meaningful comparisons, we include several baseline models our evaluation. These baselines include widely recognized models with comparable parameter sizes, such as LLaMA- 3.2, SmolLM-2, Dream, and LLaDA series. Additionally, results from the original Qwen-2.5 1.5B and 7B models, tuned with next token prediction under the same dataset and training steps, incorporated to highlight the impact of our Block Diffusion methodology.
4.2 MAIN RESULTS: PERFORMANCE AND SPEED
The 1.5B Fast-dLLM v2 achieves an average score of 45.0, outperforming the Qwen2.5-1.5B and 1ren2.5-1..B--emmm
baselines, and establishing new state-of-the-art performance among 1B- scale iiffusion-based and NTP-trained autoregressive models. At the 7B scale, Fast-dLLLM v2 reaches average score of 60.3, surpassing all baselines including Swen2.5-7B-Nemo-e and Dream (57.6), while matching or exceeding the best-performing models across most individ- ual benchmarks. These results highlight Fast-dLLM v2's consistent and strong performance across diverse tasks, while maintaining the efficiency advantages of diffusion based generation.
(59.6)
As shown in Table 1, Fast-dLLM v2 models achieve competitive performance across wide range of benchmarks. Notably, both the 1.5B and 7B variants perform on par with or better than their counterparts trained with standard next-
160
138.2
Selected
140
80
Non-Parallel Bus di
120
token prediction loss on the same data and for the same number of steps.
Accuracy (%)
75
3.36
301.7
100
Throughput
2.6 Speedup
70-
60
To balance generation quality and efficiency, we adopt a confidence-basee parallel decoding strat- egy, where each token is individually finalized once predicted confidence exceeds prede- fined threshold. As shown in Figure 4, lower threshold allows more tokens to be finalized ear- lier in the denoising process, effectively reducing the number of required decoding steps and im- proving throughput. Specifically, with thresh- old 0.9, we observe only marginal drop in GSM8K accuracy, while throughput increases
-00
0.5
0.6
0.7
0.8
0.9
1.0
Threshold
Figure 4: Accuracy and throughput under dif- ferent thresholds on GSM8K. Threshold 0.9 is selected, offering a 2.6x speedup with minimal accuracy drop.

---

Published as a conference paper at ICLR 2026
significantly from 39.1 to 101.7 tokens/s- yielding 2.6x speedup. This setting provides favor- able trade-off between performance and efficiency. Importantly, setting the threshold to 1.0 recovers the standard non con-parallel decoding process, where all tokens updated through the full sequence of denoising steps and finalized only at the end.
Figure compares the throughput of Fast-dLLM v2 (7B) and Qwen2.5-7B Instrrcct across range of batch sizes on both NVIDIA A100 and H100 GPUs for GSM8K, where we set the threshold to 0.9 and use sub-block cache. Across all settings, diffusion generation consistently outperforms the autoregressive baseline, demonstrating superior scalability with increasing batch size. On the A 100,, diffusion achieves up to 1.5x higher throughput at batch size 64, while the advantage is even more pronounced on the H100, reaching up to 1.8x speedup. This improvement highlights the efficiency benefits of diffusion decoding, especially on newer hardware architectures where parallelism can be better exploited. These results reinforce the practicality of diffusion- generation in real-world deployment scenarios where low-latency, high-throughput inference is critical.
4.3 ABLATION STUDY
We conduct all ablation experiments using the Fast-dLLM v2 1.5B model to systematically inves- tigate the impact of architectural and decoding choices. As shown in Table 2, the baseline ("naive token shift") applies strategy where, for each training block, a subset of tokens is randomly masked, and each masked token is predicted using the model's output at the preceding position. To improve training fidelity, we introduce a padding strategy ("+ pad") that appends non-loss-bearing <MASK> tokens to each training sample such that its length becomes multiple of block size. This modi- fication is crucial to preserve data integrity during sequence packing: without padding, an <EOS> token from one sample might be immediately followed by <BOS> token from the next, and since our block wise diffusion model uses bidirectional attention, this can lead to unintended attention across samples. Padding ensures clean sample boundaries, preventing cross-sample leakage during training.
We further incorporate complementary masking (CM) strategy, where the complement of each sampled mask is also used in training. This ensures that all tokens in the input receive supervision, increasing the coverage of the learning signal. The full recipe ("+ pad CM") achieves the best overall performance across benchmarks. improving the average accuracy by +3.7 points over the naive strategy. These results highlight the importance of aligning training-ting time input construction with the model's attention mechanism and masking objectives.
As shown in Table and Table 4, we explore how sub-block size and block size affect final performance. In Table 3, we observe that ad- justing the sub-block size during inference leads to notable gains, with a size of achieving the highest accuracy average across tasks. While GSM8K performs best with smaller sizes (e.g., 2), HumanEyal and HumanEval+ show improved results up to size 8, indicating that the optimal sub-block size task-dependent.
A100 Autrregressive
A1.0 Diffusion
11.00 - Autoregressiee
HL00 Diflusion
Throughput (tokenzis)
Speedup:
Speedup: 1.5x
200
In contrast, Table 4 illustrates that directly mod- ifying the block size at inference time-without aligning it to the training-time configura- tion-results in substantial performance degra- dation. For instance, GSM8K performance drops from 62.0 in the sub-block setting to 58.5 under mismatched block size, and Hu- manEval shows similar trends. This disparity underscores the importance of maintaining con- sistency between training and inference block structures. By introducing sub-block decoding strategy, able flexibly control inference granularity without violating this consistency, thereby achieving better performance across diverse benchmarks.
Figure Throughput comparison between au- toregressive and diffusion generation methods on NVIDIA A100 and H100 GPUs across varying batch sizes. Diffusion generation consistently out- performs autoregressive on both GPUs.
8

---

Published as a conference paper at ICLR 2026
Table 2: Benchmark results for different token shift strategies. "+ CM stands for "+ complemen- tary mask". The best performance for each benchmark is shown in bold, while the second-best is underlined.
Eval
MBPP
Method
GSM8K
Math
IFEval
MMLU
GPQA
Avg.
Base
Plus
Base
Plus
Naive token shift
38.4
32.9
44.4
38.6
59.0
37.3
39.9
52.9
27.9
41.3
pad
38.4
34.1
45.2
38.4
60.1
37.0
45.8
53.5
27.7
42.2
+ pad + CMM
43.9
40.2
50.0
41.3
62.0
38.1
47.0
55.1
27.7
45.0
Eacc Caraita Burn T2. Grtte
Gapee Ceche No
Throughput (tokansis)
50
Accuracy (6)
40
30
16
16
Sub-Block Size
Sub-Block Size
(a)
(b)
Figure 6: Effect of small block size and sub-block cache on model performance. (a) Accuracy remains largely unaffected by the use of sub-block cache across different block sizes and batch sizes. (b) Throughput increases as small block size grows due to higher decoding parallelism. While sub-bloed bub-block cache has negligible effect when batch size is small, it significantly improves throughput under compute- bound settings (c.g., batch size = 32).
We further study the effects of sub-block size and block cache on both accuracy and throughput, illustrated in Figure 6. In Figure 6a, we observe that increasing the drop in accuracy, consistent with previous findings in Table 3. As shown in Figure 6b, using larger sub-block sizes increases decoding throughput by decreasing the number of required sequential forward passes and exploiting greater intra-step parallelism.
size leads to slight
In addition, evaluate the impact of introducing sub-block cache. While the cache introduces negligible gains when the batch size is small (and memory bandwidth is underutilized), provides substantial speedup in the compute-bowe bound regime, such as when the batch size is 32 as shown in Figure 5. Importantly, caching has no observable effect on model accuracy (Figure 6a), confirming that it is purely afficiency-enhancin highlight that under practical batch sizes, combining larger sub-block sizes with caching yields strong performance-efficien
feature without compromising output quality. These results
benefits.
Table 3: Sub-Block size decoding improves performance, with size being optimal.
Table 4: Inference with mismatched sizes re- duces performance.
Bub-Block Size
2
16
32
Block Size
4
8
16
32
GSM8K
62.8
61.8
62.0
61.3
60.2
GSM8K
53.2
56.8
58.5
59.7
60.2
HumanEval
42.7
43.3
43,9
39.6
38.4
HumanEval
37.8
43.3
43.3
38.4
38.4
HumanEval+
39.6
40.2
40.2
36.0
34.8
HumanEval+
34.1
39.0
39.6
34.1
34.8
CONCLUSION
In this work, we presented Fast-dLLM v2, scalable block diffusion language model framework that adapts pretrained autoregressive I.IMss into efficient diffusion-style decoders for parallel text gener-

---

Published as a conference paper at ICLR 2026
ation. By integrating blockwise diffusion mechanism with complementary masking, Fast-dLLM v2 enables intra-block bidirectional context modeling while retaining the predictive capabilities of the original AR models. To address the latency of existing diffusion-basee models, further proposed a hierarchical caching strategy, consisting of a block-level cache for inter-block context reuse and DualCache-basee sub-block cache for efficient refinement within blocks, together with a parallel decoding pipeline. Extensive experiments on large-scale Swee2..5-Instrve and 7B) demonstrate that Fast-dLLM v2 achieves up to 2.5x speedup over standard AR decod- ing without loss of generation quality, consistently matching strong AR baselines while surpassing prior diffusion-based approaches in efficiency. These results highlight the potential of block diffu- sion frameworks as a practical path toward deploying high-quality, low-latency LLMs in real-world applications.
models (1.5B
REFERENCES
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 https: //arii/aaiii.////aa org/ abs 01553.09577
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 19:117911-79999 2021.
Heli Ben- Itai Gat, Daniel Severo, Niklas Nolte, and Brian Karrer. Accelerated sampling from masked diffusion models via entropy bounded unmasking. arXiv preprint trXiv:2505.2485: 2025.
Akhiad Bercovich, Itay Levy, Izik Golan, Mohammad Dabbah, Ran El- Elnia,,,, Omri Puny, Ido Galil, Zach Moshe, Tomer Ronen, Najeeb Nabwani, Ido Shahaf, Oren Tropp, Ehud Karpas, Ran Zil- berstein, Jiaqi Zeng, Soumye Singhal, Alexander Bukharin, Yian Zhang, Tugrul Konuk, Ger- ald Shen, Ameya Sunil Mahabaleshwarkar, Bilal Kartal, Yoshi Suhara, Olivier Delalleau, Zijia Chen, Zhilin Wang. David Mosallanezhad, Adi Renduchintala, Haifeng Qian, Dima Rekesh, Fei Jia, Somshubra Majumdar, Vahid Noroozi, Wasi Uddin Ahmad, Sean Narenthiran, Alek- sander Ficek, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Igor Gitman, Ivan Moshkov, Wei Du, Shubham Toshniwal, George Armstrong, Branislav Kisacanin, Matvei Novikov, Daria Gitman, Evelina Bakhturina, Jane Polak Scowcroft, John Kamalu, Dan Su, Kezhi Kong, Markus Kliegl, Rabeeh Karimi, Ying Lin, Sanjeev Satheesh, Jupinder Parmar, Pritam Gundecha, Bran- don Norick, Joseph Jennings, Shrimai Prabhumoye, Syeda Nahida Akter, Mostofa Patwary, Abhinav Khattar, Deepak Narayanan, Roger Waleffe, Jimmy Zhang, Bor- Yoing Su, Guyue Huang. Terry Kong, Parth Chadha, Sahil Jain, Christine Harvey, Elad Segal, Jining Huang, Sergey Kashirsky, Robert McQueen, Izzy Putterman, George Lam, Arun Venkatesan, Sherry Wu, Vinh Nguyen, Manoj Kilaru, Andrew Wang, Anna Warno, Abhilash Somasamudramath, Sandip Bhaskar, Maka Dong, Nave Assaf, Shahar Mor, Omer Ullman Argov, Scot Junkin, Olek- sandr Romanenko, Pedro Larroy, Monika Katariya, Marco Rovinelli, Viji Balas, Nicholas Edel- man, Anahita Bhiwandiwalla, Muthu Subramaniam, Smita Ithape. Karthik Ramamoorthy, Yut- ing Wu, Suguna Varshini Velury, Omri Almog, Joyjit Daw, Denys Fridman, Erick Galinkin, Michael Evans, Katherine Luna, Leon Derczynski, Nikki Pope, Eileen Long, Seth Schneider, Guillermo Siman, Tomasz Grzegorzek, Pablo Ribalta, Monika Katariya, Joey Conway, Trisha Saar, Ann Guan, Krzysztof Pawelec, Shyamala Prayaga, Oleksii Kuchaiev, Boris Ginsburg, Oluwatobi Olabiyi, Kari Briski, Jonathan Cohen, Bryan Catanzaro, Jonah Alben, Yonatan Geif- man, Eric Chung, and Chris Alexiuk. Llama-nemotron: Efficient reasoning models, 2025. URL ht https: / //tpaiaivvv/.////////e.///.aa.. org/ abs / 220525555.. 00949.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss. Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners, 2020. URL https: / rxiv. org/ als 2005. 141..11666 14165.
10

---

Published as a conference paper at ICLR 2026
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, 012222266-222777 2022.
Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitz- mann. Diffusion foreing: Next-token prediction meets full-sequence diffusion. Advances in Neural Information Processing Systems, 177-24811-24122 2024.
Xinhua Chen, Sitao Huang. Cong Guo, Chiyue Wei, Yintao Hc, Jianyi Zhang, Hai Li, Yiran Chen, et al. Dpad: Efficient diffusion language models with suffix dropout. arXiv preprint arXiv:2508. 14148, 2025.
Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, and Bowen Zhou. Sdar: A synergistic dif- fusion-aue -utoreerssoonnnnnnnn paradigm for scalable sequence generation, 2025. URL https:// github. com/ JetAstra/SDaat
Itai Gat, Heli Ben-Hamu, Marton Havasi, Daniel Haziza, Jeremy Reizenstein, Gabriel Synnaeve, David Lopez-Paz, Brian Karrer, and Yaron Lipman. Set block decoding is language model inference accelerator. arXiv preprint arXiv:2509.04/85, 2025.
Google DeepMind. Gemini diffusion. https: / woceepinnd..ooge//mmee.
gemini sitniiidifffsion 2025. Accessed: 2025-05-24.
Xiaochuang Han, Sachin Kumar, and Yulia Tsvetkov. Ssd-Im: Semi-autoregrrssivve simplex- based diffusion language model for text generation and modular control. arXiv preprint arXiv:2210.17432 2022.
Feng Hong, Geng Yu, Yushi Ye, Haicheng Huang, Huangjie Zheng, Ya Zhang, Yanfeng Wang, and Jiangchao Yao. Wide-in, narrow-out: Revokable decoding for efficient and effective dllms. arXiv preprint arXiv:2507. 2025.
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- 2021.
Inception Labs. Introducing mercury: The first commercial diffusion-based language model. https: httpp:://wwwwiiit www.inccetionoaa......m. www.ntroducingggereeeee 2025. Accessed: 2025- 05-24.
Guanghao Li, Zhihui Fu, Min Fang, Qibin Zhao, Ming Tang, Chun Yuan, and Jun Wang. Diffuspee: Unlocking diffusion language models for speculative decoding. arXiv preprint arXiv:2510.02358, 2025a.
Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Li Shen, Yi Liang, Soroush Vosoughi, and Shiwei Liu. Diffusion language models know the answer before decoding. arXiv preprint arXiv:2508. 19982, 2025b.
Shufan Li, Konstantinos Kallidromitis, Hritik Bansal, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Jason Kuen, Zhe Lin, Kai-Wei Chang, and Aditya Grover. Lavida: large diffusion language model for multimodal understanding. arXiv preprint arXiv:2505.16839 2025c.
Zhiyuan Liu, Yicun Yang, Yaojie Zhang Junjie Chen, Chang Zou, Qingyuan Wei, Shaobo Wang, and Linfeng Zhang. dilm-cache: Accelerating diffusion large language models with adaptive caching. arXiv preprini arXiv:2506.06290 2025.
Aaron Lou, Chenlin Meng. and Stefano Ermon. Discrete diffusion language modeling by estimating the ratios of the data distribution. arXiv preprint arXiv:2310. 10.,44444,, 2023.
Xinyin Ma. Rumpeng Yu, Gongfan Fang, and Xinchao Wang. dkv-cache: The cache for diffusion language models. arXiv preprint arXiv:2505. 15781, 2025.

---

Published as a conference paper at ICLR 2026
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, 2025. URL https://
arxiv.org/ali wttp:gggg/.//////ae//e//////// 09992.99..............9.22999992
OpenAL. ChatGPT: Optimizing Language Models for Dialogue. OpenAl blog, November 2022.
URL
Qwen, An Yang. Baosong Yang. Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https::////// /attt//arxxx aariiv org/ abs / 2412. 15115.
Alec Radford and Karthik Narasimhan. Improving language understanding by generative pre- training. 2018. URL https: //api. http://app.ssaaiiiia.ssaamo
wwt.eorrg/rooooe.. ID: :
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019. URL https://api. semanti cwccclccchllccoo ar. wwwp:ooggoooouurooo
60025533.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamies. In International conference on machine learn- ing. pp. 2265.2265. 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. arXiv preprint arXiv:2508. 02558, 2025.
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. arXiv preprint 250i:::00000993: 2025a.
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. arXiv preprint 250i::2500.09992 2025b.
Qingyan Wei, Yaojie Zhang. Zhiyuan Liu, Dongrui Liu, and Linfeng Zhang. Accelerating diffu- sion large language models with slowfast sampling: The three golden principles. arXiv preprint arXiv:2506. 10848, 2025.
Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dllm: Training- acceleration of diffusion Ilm by enabling kv cache and parallel decoding, 2025. URL https::///// aaaaaaaxvvvv...aaa.aaaa...aa. org/ abs 2.505.226610
Tong Wu, Zhihao Fan, Xiao Liu, Hai-Tao Zheng, Yeyun Gong, Jian Jiao, Juntao Li, Jian Guo, Nan Duan, Weizhu Chen, et al. Ar-diffusion: Auto-regressive diffusion model for text generation. Advances in Neural Information Processing Systems, 36:39957-39974 2023.
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong, Dream 7b, 2025a. URL https: http///kknppppp//p//// github. wtchhhuu.oo//boooo 2025 dream.
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng 2025b.
Kong, Dream 7b: Diffusion large language models. arXiv preprint
Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. Proceed- ings of the Computer Vision and Pattern Recognition Conference, pp. 22963-22974, 2025.
Runpeng Yu, Xinyin Ma, and Xinchao Wang. Dimple: Discrete diffusion multimodal large language model with parallel decoding. arXiv preprint arXiv:2505.16990 16990, 2025.
Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu. Jun Hu, Jun Zhou, Jianfei Chen, Yankai Lin, Ji-Rong Wen, et al. Llada 1.5: Variance-reduced preference optimization for large language diffusion models. arXiv preprint arXiv:2505. 2025.
12

---

Published as a conference paper at ICLR 2026
A IMPLEMENTATION DETAILS
A.1 TRAINING SETUP
We fine-tune pretrained Owen2.5-Instruce models (1.5B and 7B) under block-wise diffusion training framework. Unless otherwise specified, all experiments adopt a context length of 2048 and batch size of 256. Training is conducted 64 NVIDIA A100 GPUs using DeepSpeed Zero-3.
Training Data. Our models are fine-tuned on a subset of the LLaMA-Nemotron post-training dataset, which contains high-quality anstruttionfoolloiing examples covering broad range of do- mains. We preprocess the dataset using block-wise packing, and pad each sequence to multiple of the block size avoid misaligned block boundaries. Redundant padding tokens excluded from loss computation and gradient updates.
Hyperparameters. The 1.5B model is trained for 6,000 steps with learning rate of 2 10-5, while the 7B model is trained for 2,500 steps with a learning rate of x 10-5 both settings, we AdamW the optimizer and apply linear learning rate warmup over the first 500 steps. With context length of 2048 and batch size of 256, each training step processes 256 x 2048 524,288 tokens. This corresponds total training token count of approximately:
1,5B model: 6,000 524,288 3.15 billion tokens
7B model: 2,500 x 524,288 x 1.31 billion tokens
We fix the block size to 32 for all experiments. All training sequences are right-padded and packed a block-aligned fashion fully utilize model context, enabling consistent and efficient batch construction under hardware constraints.
A.2
ATTENTION MASK DESIGN
Block Diagonal
Attention Computation
x
Offiet Block Causal
47
Block Causal
Cached
X7
x
x
x63
x²
x3
X2
-22
Xg
X6
x
(a) Training-time attention mask.
(b) Inference-time attention mask.
Figure 7: Specialized attention mask design for diffusion language modeling. (a) During training, each input consists of corrupted sequence Tr and corresponding targets no. concatenated and pro- cessed in a single forward pass. The attention mask combines intra-block bidirectional attention (Block Diagonal), cross-block causal dependency from clean tokens to noised ones (Offset Block Causal), and traditional left-to-right causality among clean tokens (Block Causal). (b) During in- ference, previously decoded blocks of 2i are reused via caching. Only the current noised block a is computed in each decoding step, which attends to cached prefixes (shaded) and updates its own block self-contained fashion.
To enable efficient and structured learning across both corrupted and clean views of the input, we use custom block-awart aware attention scheme (Arriola et al., 2025). At each training step, concatenate the noised sequence F, and the clean sequence in into single input total length 2L, then apply hybrid attention pattern defined via attention mask Mnnll {0, 12222L
13

---

Published as a conference paper at ICLR 2026
To simplify notation, follow prior work and slightly abuse the symbol z², which in this context denotes the set of tokens in the b-th block (rather than the b-th token, as earlier sections). Specif- ically, we aim to model the conditional probabilities Pa(a" x =b) across all blocks b E [1, B], where zy the noised version of block b, and x comprises all clean tokens in previous blocks. This formulation enables us to process both noised and clean representations simultaneously by feeding their concatenated sequence into the transformer and applying a carefully constructed atten- tion mask Mful as shown in Figure 7(a).
The overall attention mask can be decomposed into four sub-masks:
MBD
MOBC MBC
Minl
0
where:
MBD (Block-diagonal mask): Provides bidirectional self-attention among tokens within the same block the noised sequence Iy, enabling within-block refinement:
[MDD]y =00
if i,.j belong the same block
0
otherwise
MMoc (Offset block-causal mask): Allows each noised token in a to attend to tokens from previous blocks in the clean sequence zo. preserving block causal conditioning:
[Mocll {1
i is in a block before
otherwise
-Mc calock-causal mask): Enables each token in the clean sequence zo to attend to all previous and current block positions, facilitating autoregressive-like progression:
{
j is in the same an earlier block otherwise
[Mecly
The combined mask allows unified handling of masked token prediction, simultancous conditioning on prior known context, and structural training efficiency via Mlock-parallelism.
During inference, we adopt a simplified causal attention mechanism that reuses decoded blocks frozen prefix context. As illustrated in Figure 7(b), previously generated blocks from z6 cached to avoid redundant computation, and only the current noised block x is actively refined. This block attends bidirectionally within itself, similar to MED during training, while attending causally the unmasked tokens in previous blocks. The attention computation is thus restricted to the current block and its causal prefix, enabling efficient decoding via key-value cache reuse and reduced memory footprint. This structure preserves left-to-right semantics across blocks while allowing intra- block denoising in parallel.
A.3 DETAILS ON TRAINING OBJECTIVE
We minimize the masked-token--nll cross-entropy loss:
Lolock (0) -- -E2,m
1|x [MASK]] log (ath)
I (blockk))
Notably, this objective function seems to omit the normalization coefficient often found in standard masked modeling losses (e.g., dividing by the number of masked tokens). This is intentional and justified by our complementary masking strategy. This is because we use a complementary mask for each training sample zo; we always sample two complementary times / and / with mask and m m.
Σ 1 = [MASK] log (e6 wei, (oooe()))
Σ 1|ri- = [MASK]] log log æei. mooosss)
Due to the complementary mask, the total number of tokens contributing to the loss for any given sample 2i is always the full sequence length L.
14

---

Published as a conference paper at ICLR 2026
A.4 EVALUATION PROTOCOL
We evaluate all trained models on a diverse suite of downstream benchmarks covering reasoning, knowledge, and code generation. Unless otherwise specified, all evaluations are conducted using greedy decoding (a rgmax). We adopt zero-shot settings for all tasks, with the exception of GPQA, which is evaluated under 5-shot prompting following standard protocol.
All non-code tasks are evaluated using the LM-Eval harness, ensuring compatibility and fair perfor- mance reporting. For code tasks like HumanEval and MBPP, we employ the EvalPlus framework for reliable pass-rate rate calculation. Unless otherwise noted, the following setup is used during inference:
Block size 32
Sub-block size = 8
Parallel decoding disabled (threshold 1)
This configuration ensures consistency between training and inference setups, facilitating effective evaluation of the block-wise diffusion capability in Fast-dLLM v2.
B
CASE STUDY
To better illustrate the reasoning and interaction capabilities of Fast-dLLM v2 (7B), conducted detailed examination of both single-turn and multi-turn dialogue scenarios. Representative examples presented in Table 5 and Table 6.
Single-turn Dialogue Scenarios. As shown in Table 5, Fast-dLLM v2 is capable of handling com- plex queries in single interaction. In the Math example, the model correctly analyzes the rational function to determine the number of vertical asymptotes, applying algebraic factoring and solving for the undefined values of x. In the Code section, the model generates correct and recursive Python implementation of the Tower of Hanoi problem, along with an appropriate explanation of the input parameters and output.
Multi-turn Dialogue Scenarios. Table 6 highlights multi-turn dialogues where Fast-dLLM v2 retains context and builds upon previous turns. The Daily life example illustrates the model's ability to perform temporal reasoning, such as computing the number of years since company was founded and determining future anniversary. The Math example showcases step-by-step logical reasoning to solve real-world arithmetic problem involving truckload capacity and total delivery time. The model effectively breaks the problem into sequential steps, performs intermediate calculations, and presents the final result in the required format (hours and minutes).
These case studies collectively demonstrate Fast-dLLM v2's strength in mathematical reasoning, code generation, temporal understanding, and contextual coherence across both single-turn and multi-turn settings.
C ADDITIONAL EXPERIMENTS
C. LATENCY AND THROUGHPUT SCALING WITH CONTEXT LENGTH
To evaluate the scalability of our method in deployment scenarios involving longer inputs, we ex- tended our testing beyond the sequence lengths reported in the main text. We conducted experiments varying the context length from 1K to 8K tokens, measuring both generation latency and throughput for generating 32 completion tokens. All tests were performed on single NVIDIA A100 80GB GPU.
As illustrated in Figure 8, our hierarchical cache consistently outperforms block-only cache, partic- ularly at longer contexts. Notably, it also achieves latency and throughput comparable to standard AR model caching, showing that our caching system is highly effective in reducing overhead even under blockwise diffusion decoding. Furthermore, when combined with our parallel decoding strat- egy (i.e., sub-block speculative denoising), the overall method achieves 2-3x speedup over strong AR baselines shown in Figure of the main paper.
15

---

Published as a conference paper at ICLR 2026
Generation
Generation Comparison
Al
NEN inteachiia sache (Elck--ache
catckkcchh
2.5
275
2.5
Spoueh (omeroul
12
10
15.0
Figure 8: Latency and Throughput Scaling. Comparison of generation latency and throughput across context lengths ranging from 1K to 8K tokens. Our hierarchical cache maintains high effi- ciency as context length increases.
D LLM USAGE
During manuscript preparation, we used large language models -strictly for language polishing of paragraphs and sentences (grammar, flow, and tone). These tools were not used to generate ideas, design experiments, or determine conclusions. All technical content, methodology, and interpreta- tions were written, verified, and approved by the authors. To reduce risks of factual drift or citation errors, required human review of every model-edited sentence and cross-checked all references against primary sources. The authors take full responsibility for the accuracy and integrity of the manuscript.
16

---

Published as a conference paper at ICLR 2026
Table 5: Single-turn Dialogue Cases of Fast-dLLM v2 (7B).
Math
User
How many vertical asymptotes does the graph of 2=42-6 have?
Assistant
To determine the number of vertical asymptotes of the function y = 2 we need to identity the values of that make the denominator zero, these values will cause the function approach infinity, indicating a vertical asymptote.
First, we factor the denominator x² + + 1 6. We look for two numbers that multiply to -6 and add up to 1. These numbers are and -2 Therefore, we can factor the quadratic as follows:
x² + 6 = (or + 3] 2)
So the function can be rewritten
y (x: + 3 2)
The denominator is zero when x + = 0 or = 0. Solving these equations, we get:
3 and
2
Since the function is undefined at x = 3 and r = 2, there are vertical asymptotes at these points. Therefore, the function 2 has two vertical asymptotes.
2
The number vertical asymptotes
Code
User
Write recursive Python function to solve the Tower of Hanoi problem and print each move.
Assistant
def tower_ce saatr_ff_aaate hanoi (n, source, target, auxiliary):
if 0:
tower_of hanoiiii (n 1, source, auxiliary, target) to,
print (f"Mooe__Mveeeeeee tcwer_of Laver_of_hanoi (n 1, auxiliary, target, source)
tower_of_hand (3,
' (B')
This function solves Tower Hanoi problem using recursion. takes four parameters: (the number of disks), source (the starting peg), t target (the destination peg), and auxi miilli liary (the helper peg). The function prints cach move in the form of "Move disk n from source to target".
17

---

Published as a conference paper at ICLR 2026
Table 6: Multi-turn Dialogue Cases of Fast-dLLM v2 (7B).
Daily life
User
When NVIDIA founded?
Assistant
NVIDIA was founded 1993.
User
Using the year you just gave calculate how many years NVIDIA has been in business as of 2025.
Assistant
NVIDIA has been in business for 32 years as of 2025.
User
How many years will be until NVIDIA's 40th anniversary?
Assistant
NVIDIA's 40th anniversary will 2033, which years from now.
Math
User
A delivery truck can carry 120 boxes in one trip. If a warchouse needs to send out 960 boxes, how many trips will the truck need?
Assistant
determine how many trips delivery truck will need send out 960 boxes, we start by noting the capacity of the truck in one trip. The truck can carry 120 boxes in one trip. We need to find out how many trips required to transport 960 boxes. We set up the problem division problem:
Total number of boxes
Number of trips
Capacity of the truck per trip
Substituting the given values into the equation, we get:
Number of trips 120
Next, we perform the division:
960
120
Therefore, the truck will need 8 trips to send out 960 boxes.
User
If each trip takes 45 minutes, how long will it take to deliver all boxes in total? Give your answer in hours and minutes.
Assistant
To determine the total time required deliver all the boxes, we need follow these steps: Calculate number of trips required.
2. Determine the total time taken for all the trips.
3. Convert the total time from minutes to hours and minutes.
Step 1: Calculate the number of trips required.
The warchouse needs send 960 boxes, and the truck can carry 120 boxes per trip. Therefore, the number of trips required is:
960
Number of trips
120
Determine the total time taken for all the trips. Each trip takes 45 minutes, the total time taken for trips is:
Step 2:
Total time = x 45 = 360 minutes
Step Convert the total time from minutes to hours and minutes.
There are minutes an hour, so convert 360 minutes to hours by dividing by
360 + 60 6 hours
Therefore, the total time required deliver all the boxes is:
hours
18


Reviews:
[{"review_id": "fWSibuZIbS", "reviewer": "Reviewer_nsrh", "summary": "The paper introduces a block-diffusion adaptation to convert a pretrained AR model into a block-wise parallel generation model, achieving up to 2.5× speed-up over standard AR decoding with minimal loss in accuracy.\n\nContributions include: introducing complementary masking during training, a \"hierarchical\" KV caching mechanism to enable efficient block decoding, and large-scale evaluations on models upto 7B in size.", "questions": "- i'm still confused on how the hierarchical caching mechanism works, could the authors provide more details?\n- are different sub-blocks decoded autoregressively? or in a given block, different sub-blocks can be decoded in any order?\n- why do you need sub-blocks? can't dual cache work within a block, without defining sub-blocks?\n- how would the latency change when the context length increases? i think authors tested on sequence lengths upto 2K only, i'm very curious to see how the latency scales with increasing context length\n\n\nSince i'm not an expert in this field, i'll be looking at feedback of other reviewers too to gain a better understanding.\nThat being said, i'm definitely willing to improve my score during our discussion :)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- requires light weight finetuning to convert AR to diffusion models\n- better and faster than DREAM style approaches, that utilizes full sequence diffusion for generation\n- there is a configuration in Fast-dLLM v2 that offers speed ups compared to an AR model, with similar accuracy -- fulfiling the promise of diffusion models", "weaknesses": "My main concern is limited conceptual novelty: there is only a single change from the previous iteration of Fast-dLLM-v1, which is adding blockwise training and generation. This weakness is not a bad thing in itself.\nPlease look at the questions :)\n\nOther weakness is: the cache is still an approximation.", "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": "0PM6rwMDFf", "reviewer": "Reviewer_3sof", "summary": "This work introduces Fast-dLLM v2, which turns regular AR language models into faster diffusion models that can generate multiple tokens at once, using only ~1B tokens for fine-tuning. The key trick is a block-based approach where tokens within each block can look at each other while still respecting the left-to-right flow between blocks. With smart caching and parallel decoding, it runs up to 2.5× faster than standard models while keeping the same quality on benchmarks.", "questions": "1. What could be causing the consistent and significant discrepancy mentioned in \"Weakness\"?\n2. Can this block diffusion process be used together with existing methods to accelerate AR models? eg. speculative decoding.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The method is elegantly simple yet effective. It adapts pretrained AR models into block diffusion models with minimal modifications, requiring only a complementary masking strategy and block-wise attention that can be easily integrated into existing training and inference frameworks.\n2. The paper provides thorough experimental validation across multiple model scales and diverse benchmarks, including code generation, mathematical reasoning, and knowledge tasks, with comprehensive ablation studies examining the impact of block sizes, sub-block sizes, and caching strategies.\n3. The presentation is easy to follow, with concrete experimental details that make the work solid.", "weaknesses": "1. Although the average performance remains the same compared with Nemo-FT models, there are consistent patterns in performance change in specific benchmarks. Eg. HumanEval has a significant increase, while Math experiences a large decrease in both 1.5B and 7B comparison, which is unlikely to be random fluctuations.\n2. While causing some performance drops in some of the benchmarks, the proposed method only achieves 2.5x acceleration compared with AR models. The speed-up is similar to many existing acceleration methods for AR models, like speculative decoding, making the method less competitive.", "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": "blwnpzdG4V", "reviewer": "Reviewer_9tWZ", "summary": "This paper proposes Fast-dLLM v2, a block diffusion language model (dLLM) designed for efficient inference. The core idea is to adapt a pretrained autoregressive (AR) LLM into a block-wise diffusion model using a data-efficient finetuning process (requiring only ~1B tokens). The method introduces a novel training recipe with a complementary attention mask to enable this adaptation. For inference acceleration, the paper proposes a hierarchical caching mechanism: a block-level cache for reusing context from previous blocks and a sub-block cache (adopting DualCache from Fast-dLLM v1) for parallel generation within the current block. Experiments show that Fast-dLLM v2 can achieve up to a 2.5x speedup over its AR baseline (Qwen2.5-7B) while maintaining comparable or slightly improved accuracy on several benchmarks.", "questions": "1. Could the authors please elaborate on the novelty of the hierarchical caching mechanism? Is there more to it than the combination of the inter-block cache from Block Diffusion and the intra-block DualCache from Fast-dLLM v1?\n\n2. The data efficiency (1B vs. 580B tokens) is a key claim over Dream. How much of this 500x reduction is due to the proposed training recipe (complementary masks, etc.) versus the simpler, more AR-like target architecture (block-wise dLLM) compared to Dream's full-attention dLLM?\n\n3. Could the authors clarify the primary novel contributions of their finetuning approach and inference pipeline compared to the concurrent SDAR (Cheng et al., 2025), which also finetunes a pretrained AR model into a block diffusion model?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Significant Problem: The paper addresses the critical problem of LLM inference latency. The goal of achieving parallel decoding speedups while preserving the quality of strong AR models is highly relevant.\n\n2. Strong Empirical Results: The method demonstrates impressive practical results. A 2.5x speedup over a strong AR baseline like Qwen2.5-7B-Instruct with no loss in generation quality is a significant engineering achievement.\n\n3. Data and Training Efficiency: A major strength is the data efficiency of the finetuning process. Adapting a 7B model with only ~1B tokens is a practical and accessible approach compared to training full-attention dLLMs, which the paper notes can require orders of magnitude more data (e.g., 580B tokens for Dream).\n\n4. Clarity: The paper is well-written, and the proposed architecture, training process, and inference pipeline are explained clearly.", "weaknesses": "The primary weakness of this paper is its perceived lack of conceptually new contribution, as it appears to be a skillful integration of several existing lines of work.\n\n1. Combination of Existing Caching Techniques: The \"hierarchical caching mechanism\" seems to be a straightforward combination of two existing ideas.\n- The block-level cache, which stores representations of previously decoded blocks, is an inherent feature of the block-wise autoregressive structure, as seen in \"Block Diffusion\" (Arriola et al., 2025).\n- The sub-block cache for parallel refinement within a block is explicitly stated to \"adopt the DualCache in Fast-dLLM (Wu et al., 2025)\" (Section 3.3).\n- While effective, combining these two caches (inter-block from Block Diffusion, intra-block from Fast-dLLM v1) feels more like an incremental engineering step than a novel caching design.\n\n2. Finetuning Concept is Not New: The core idea of adapting a pretrained AR model into a diffusion model has been explored before.\n\n- Previous work Dream (Ye et al., 2025b;a) is already a model that was \"adapted from the existing Qwen-2.5 7B\" (Section 2.1), and the paper also points to \"SDAR (Cheng et al., 2025)\" as concurrent work that \"successfully finetuned a block diffusion model from a pretrained autoregressive model\" (Section 2.2).\n- The main claim over Dream is data efficiency (1B vs. 580B tokens). However, this comparison may not be direct, as Dream adapts to a full-attention dLLM, whereas this work adapts to a more structured block-wise dLLM. The efficiency gain may stem more from the architectural choice (block-wise vs. full-attention) than from a fundamentally new finetuning recipe.\n\nOverall, while the final system is fast and effective, the contributions seem to be more in the successful engineering and combination of existing components rather than the introduction of new, fundamental concepts.", "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": "mqzJouCUuT", "reviewer": "Reviewer_aB3q", "summary": "This paper introduces Fast-dLLM v2, a block diffusion language model designed to address the inherent inefficiency of sequential decoding in standard autoregressive LLMs. Its core contribution is a highly data-efficient adaptation method that converts pre-trained AR models into efficient parallel decoders, requiring around ~1B tokens for the entire fine-tuning process. This represents a 500x reduction in required training data compared to full-attention dLLMs such as Dream, while successfully preserving the original model's performance. The method employs a novel training recipe combining a block diffusion mechanism with a complementary attention mask to enable block-wise bidirectional context modeling. To accelerate inference, it introduces a hierarchical caching mechanism: a \"block-level cache\" for inter-block context reuse, and a DualCache-based \"sub-block cache\" to facilitate efficient parallel refinement within each block. Extensive experiments demonstrate that Fast-dLLM v2 achieves up to a 2.5x inference speedup compared to standard AR decoding. Crucially, this acceleration is achieved without sacrificing generation quality; the model's performance generally matches or exceeds that of strong AR baselines, while varying among different tasks.", "questions": "1.The proposed method includes a hierarchical caching mechanism and a specialized hybrid attention mask; will this lead to a more complex engineering implementation?\n\n2.The adaptation is performed using the LLaMA-Nemotron dataset, which consists of high-quality SFT data. This raises the question of whether such high-quality instruction data is a necessary component for successfully learning the block diffusion mechanism, or if the method could be equally effective using general pre-training corpora.\n\n3.Could the same impressive results be achieved if other mainstream AR architectures were used for the pre-trained initialization? Or is the success of this method dependent on the specific architecture or quality of the AR model used?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1.The paper proposes a novel and ingenious method. The paper's design of the hierarchical caching mechanism is ingenious, maximizing the dLLM's intra-block parallel decoding potential while preserving the AR model's inter-block causal dependencies.\n\n2.The paper's results are outstanding. The proposed training method is highly efficient and achieves SOTA inference speed without sacrificing quality. This greatly reduces training costs and holds significant practical value.\n\n3.The experimental analysis is thorough and detailed. For instance, it provides a practical trade-off analysis of the \"confidence threshold,\" which is highly instructive for the technology's real-world deployment.\n\n4.The technical details and experimental settings are clearly articulated, facilitating reproducibility.\n\n5.The paper's writing and organizational structure are very clear, demonstrating excellent readability.\n\nOverall, this is a solid and good paper.", "weaknesses": "The paper claims that token shift strategy is necessary for preserving the pretrained AR model’s representation quality. However, this claim is not empirically validated with a dedicated ablation study. As shown in Table 2, the most basic baseline, 'naive token shift,' already includes this strategy. Therefore, the actual necessity of this design is unclear.", "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)