Planned Diffusion
Authors: Daniel Mingyi Israel
Tian Jin
Ellie Y Cheng
Guy Van den Broeck
Aditya Grover
Suvinay Subramanian
Michael Carbin
Venue: ICLR 2026 Poster (2026)
Keywords: diffusion
LLM
parallel generation
fast inference
autoregressive
planning
hybrid model
Primary area: foundation or frontier models, including LLMs
TL;DR: Planned diffusion speeds up LLM inference by denoising parallelized spans from a previously generated plan.

Abstract: Most existing large language models are autoregressive: they generate text one token at a time, and cannot decode any new tokens until they have decoded every token before it.
Discrete diffusion language models offer a promising alternative by generating multiple tokens in parallel, but sampling from them requires a _denoising order_, the strategy for deciding which tokens to decode at each step.
Determining the right denoising order is difficult, and existing approaches use heuristics that create a steep trade-off between quality and latency.
We propose _planned diffusion_, a system that trains the model to determine its own denoising order.
Planned diffusion uses a single model that transitions between autoregressive and diffusion-based generation: first, the model autoregressively generates a plan that partitions the response into semantically independent chunks, defining a denoising order that parallelizes sampling across chunks; second, the model executes this plan via diffusion denoising.
On AlpacaEval, a suite of 805 instruction-following prompts, planned diffusion achieves Pareto-optimal trade-off between quality and latency, achieving 1.27x to 1.81x speedup over autoregressive generation with only 0.87\% to 5.4\% drop in win rate.
Our empirical results show that planned diffusion exhibits superior performance scaling on downstream tasks compared to autoregressive baselines while offering the runtime flexibility to precisely navigate the quality-latency trade-off.

Published as a conference paper ICLR 2026
PLANNED DIFFUSION
Daniel Israel !+
Tian Jin
Ellie Cheng²
Guy Van den Broeck' Michael Carbin²
Aditya Grover'
Suvinay Subramanian
University of California, Los Angeles
MIT CSAIL
Google
ABSTRACT
Most existing large language models are autoregressive: they generate text one token at time, and cannot decode any new tokens until they have decoded every token before it. Discrete diffusion language models offer a promising alternative by generating multiple tokens in parallel, but sampling from them requires denoising order, the strategy for deciding which tokens to decode at each step. Determining the right denoising order is difficult, and existing approaches use heuristics that create a steep trade-off' between qual- ity and latency. We propose planned diffusion, a system that trains the model to determine its own denoising order. Planned diffusion uses single model that transitions between au- toregressive and diffusion-
generation: first, the model autoregressively generates a plan that partitions the response into semantically independent chunks, defining a denois- ing order that parallelizes sampling across chunks; second, the model executes this plan prompts,
via diffusion denoising. On AlpacaEval, a suite of 805 mnstruction-folloviin planned diffusion achieves Pareto-optimal trade-off between quality and latency, achiev- ing 1.27x to 1.81x speedup over autoregressive generation with only 0.87% to 5.4% drop in win rate. Our empirical results show that planned diffusion exhibits superior perfor- mance scaling on downstream tasks compared to autoregressive baselines while offering the runtime flexibility to precisely navigate the quality-latency trade-off.
1 INTRODUCTION
Large language models achieve strong performance diverse tasks such open-ended dialogue (Ouyang let al. 2022), code generation (Chen et al.] 202I). and mathematical reasoning (Lewkowycz et al. 2022). Most existing large language models are autoregressive (Brown et al., 2020 Chowdhery al.) 2023 Tou- vron et al. 2023): they generate text one token at time, and cannot decode any token until they have de- coded every token before it. This sequential dependence between decoding steps within response creates a fundamental latency bottleneck. Discrete diffusion language models (Sahoo et al. (024) take a different approach: they decode multiple tokens per step. However, sampling from discrete diflusion models requires a denoising order: the strategy for deciding which tokens to decode at each step. Determining the right de- noising order difficult, and existing approaches use heuristics such as random order or confidence-based thresholds, creating a steep trade-off between quality and latency (Wu et al. (025).
We propose planned diffisson,, a system that trains the model to determine its own denoising order. Typical language model responses contain semantically independent chunks that do not need to be denoised sequen- tially. For example, in a response with a bulleted list, the model can denoise each bullet point concurrently (Ning et al. 2024). Planned diffusion exploits this by having the model identify independent chunks itself and denoise tokens in different chunks in parallel.
"Equal contribution.

---

Published as conference paper ICLR 2026
Autoregressive Plan
3) Diffusion Denaise
What Borealis? concise.
defiitionn
Aurora Borealis, the Northern Lights, is natural Light display in Earth's 's upper atnosphere triggered by solar activity.
<topic="definntion" Len-30>
doppice"deccrpption 1en=30>
stopic="locationn Len=18>
Span 5 dese pion
<e0s>>
It appears as noving curtains or ares of colored light charged particles from the Sun excite atmospheric gases.
Programmates Scaffold
[M] 130 total)
is
the Arctic Circle.
Sown description
Assistant: Aurora Borealis (Northern Lights) natural Light display Earth's upper atnosphere during solar activity. looks like moving curtains ares colored light caused by charged particles exciting atmospheric gases, and the Arctic Circle.
[M] 130 total)
ocation
[M] [M] [M]
[10 total)
Figure 1: A real example of planned diffusion. (1) The model first generates a sequential plan using control tags to define the structure and length of independent text chunks. (2) The runtime then translates this plan into scaffold, initializing each chunk with corresponding number of mask tokens. (3) The model denoises all chunks in parallel with diffusion, generating the text for each section simultaneously to produce the complete response.
Planned diffusion uses a single model that transitions between autoregressive and diffusion-based generation. First, in an autoregressive planning stage, the model sequentially generates generation plan composed of structural control tags. This plan partitions the response into a set of semantically independent chunks, defining denoising order so that tokens in different chunks can denoise parallel. Second, in a parallel diffusion stage, the model executes this denoising order, filling in all planned chunks simultaneously via diffusion denoising. This single-model, hybrid approach offers a distinct architectural advantage over other acceleration techniques, such speculative decoding (Leviathan al. 2023), which requires separate draft model. To the best of our knowledge, this is the first text-only model trained with both discrete diffusion and autoregressive objectives. Figure presents sample generation produced by planned diffusion model.
We make the following contributions:
1. We introduce planned diffiusion, into a sequential planning stage and a parallel diffusion stage.
parallel generation technique that decomposes text generation
2. We design the control tag language, model training methodology, and inference algorithm that enable single model perform this hybrid generation process.
3. We demonstrate that planned diffusion achieves Pareto-optimal trade-off between quality and latency AlpacaEvval, a suite of 805 corruction-folloiinn prompts; it achieves 1.27x 1.81x speedup over autoregressive generation while incurring only a 0.87% to 5.4% drop in win rate, respectively.
4. We present sensitivity analysis that validates that the planning mechanism of our model is minimal and reliable, and show that simple runtime knobs offer tunable control over the quality-latency trade-off.
2

---

Published as a conference paper ICLR 2026
2
RELATED WORK
Our work builds upon recent developments non-autoregressive and parallel decoding strategies. We po- sition contributions in the context of two primary research areas: diffusion-based language models and methods for achieving semantic parallelism.
Diffusion Language Models. Diffusion models have recently emerged new paradigm for generative language tasks (Austin et a 2021 Sahoo et al. 2024). A significant body of research is focused on accel-
erating the inference process, which traditionally involves many iterative denoising steps (Liu al. 2025a).
These acceleration techniques include KV caching for diffusion models (Ma ct al.
2025 Liu ct al. 2025d),
the use of autoregressive verification (Hu et al. 2025 Israel et al. 2025a). and the development of fast sam- pling strategies that reduce the number of required steps (Wu et al. |025 Li et al. [025 HHon et al. (025). Our proposed planned diffusion framework orthogonal and complementary to these methods; any diffu- sion sampling strategy can be integrated into the diffusion component of our algorithm to further increase performance. Other related work include block diffusion (Arriola et al. 2025) which enforces an autore- gressive structure over blocks and planned denoising (Liu et al. (022cc) which learns an adaptive denoising schedule. While relevant, neither targets semantic parallelism, which is the purpose of planned diffusion.
Semantic Parallelism. While diffusion models achieve token-wiss parallelism, they not trained to achieve parallelism across larger portions of text at the semantic level. We define semantic parallelism broad class of techniques that produce models capable of parallelizing over semantically independent
chunks of tokens. Many recent works explore semantic parallelism (Ning et al., 2024 Liu et al., [0224] Jin
2025 Rodionov al. 2025 Pan et al. 2025 Wen al., 2025 Yang et al. 2025). Existing works et al. operate within a purely autoregressive framework, while to the best of our knowledge, our work is the first to propose a hybrid
oifffuuiii model for text (see Appendix foro more thorough discussion).
By combining an autoregressive planning phase with a parallel diffusion phase, our method utilizes diffusion parallelism a structured manner corresponding semantic parallelism, presenting novel approach for efficient text generation.
Other Parallel Generation Techniques. There is a rich line of work on parallel generation. Insertion- based models parallelize by inserting tokens into partial positions, reducing decoding depth via simultaneous
insertion updates (Stern et af.) 2019). Speculative decoding accelerates autoregressive models by drafting multiple tokens and verifying them in parallel (Leviathan al. [2023] Chen et al. [2023] Zhang et al. (024)).
Planned diffusion differs conceptually as we use single hybrid model for both autoregressive and parallel generation.
3 PRELIMINARIES
Generative language models learn probability distribution over sequences of discrete tokens. In this work, our focus is on two main paradigms: autoregressive and discrete diffusson.
Autoregression. Autoregressive models are the standard for sequential text generation. They factorize the joint probability of a token sequence x = (I1, T2, ,I). The probability distribution under this model, which we denote as PAR, is given by:
|z|
PAR (x)
II po (0(
(I)
where Po is a parameterized conditional distribution over tokens. In autoregression, each token sequen- tially sampled conditioned on all previously generated tokens.
3

---

Published as conference paper ICLR 2026
Discrete Diffusion. Discrete diffusion models learn reverse fixed data corruption process that grad- ually introduces noise into a clean sequence. For text, this involves incrementally replacing tokens with a special mask token (Austin al., (021]. Let æ be clean sequence of tokens. The forward corruption process produces noisy version at noise level t E [0, 1], masking each position independently.
if [MASK]
(0 | 14)
3 24
2njo x)) II (:j m4).
otherwise
Because the true posterior over the forward noising process is intractable (Lou et al. 2024), diffusion models learn an approximation by maximizing a variational lower bound on the log-likelihood (Sahoo et al. 2024).
log)p(x) 2 E.---r0.1. Σ 1 [MASK]) po [
(3)
key property trained diffusion model is that for any disjoint sets of positions O, Q S [L], we can obtain conditional distribution pD over queried tokens at positions Q given observed tokens at positions O. This conditional distribution factorizes as follows
II iE
PD x0o).
(4)
In planned diffusion, will assume PAR and pD can be conditioned prior context tokens (C1, C2, .2. Thus, we shall utilize the distributions PAR(- c) and pb(- c) respectively.
Sampling from discrete diffusion model requires selecting a denoising order (Kim) et al. 2025 Turok et al. 2026). We first define an ordered partition of positions [L] = (l....,,), L} as a tuple
Denoising Order.
(0 σr) of non-empty disjoint subsets with U q. = [4]. We write det = 01 U .... の- for positions revealed before step Positions within the same subset decode in parallel; positions different subsets decode sequentially. Thus, denoising order is procedure that maps available inputs to an ordered partition of [Z.]. Any ordered partition a and prefix tokens imply a joint distribution
Po(x σ) = II PD (Zo. Io.
(5)
obtained by substituting O = Dct and Q = a into Equation We shall define uniform and entropy based ordering. For the sake of simplicity, we will only define the orderings where |o, for all Let be a permutation of [L] drawn uniformly at random. The uniform ordered partition is
Unif == {T{(t)
t= t,.,..,,
Uniform random unmasking is the denoising order implied by the training objective in Equation but in practice it does not achieve the best results compared to entropy ordered unmasking (Israel et al. 2025a The entropy ordered partition is constructed such that at each step selects the lowest predicted token entropy
Ent =
arg min H(pD(aa (ar))) io
t= 1,,...,, L
where H denotes Shannon entropy. Unlike Unif the partition not fixed in advance but depends on the tokens revealed at each step. This is the default inference strategy Ye al. (2025).
4

---

Published as conference paper ICLR 2026
4 PLANNED DIFFUSION
Planned diffusion combines an autoregressively sampled plan with discrete diffusion. The plan segments the output into semantically independent chunks and conditions the content of each; the diffusion model then fills in the chunks in parallel, each of which with any denoising order. Unlike denoising order, which can govern only when and where each position is sampled, the plan can also determine what sampled in parallel.
4.1 FORMAL DESCRIPTION
We formalize single iteration of planned diffusion. Beginning from prior tokens the model autoregres- sively generates plan z, which specifies K chunks with lengths 4,..., This induces total sequence K length L = Σx= l and a partition of [L] into contiguous chunks $,,..., SK. where = {4-1 + 1 4. for k = ,,.... K. We can define a denoising order for each chunk by letting ((k) be any ordered partition 5% into exactly 4 steps. Thus, planned diffusion admits joint distribution over plan and tokens x generated in order (z) conditioned on previous tokens
Plan (2)
U
(k)
t=
max l k
(8)
(k:15k+)
which reveals one position from every chunk Sk with 4 2 simultaneously at step t. The diffusion phase generates content vr, and the joint distribution over plan and content is:
Pro(z,< c; of Plan(2)) = PAn(= 1e)ccc)-pppp 2, c of Plan (z))
Planning
Diflusion
Because accepts any valid ordered partition of [L], the choice of o (k) is fully decoupled from the model. Setting each (*) = Ent restricted to S₁, recovers the entropy strategy applied independently within each chunk, which the configuration used our experiments. The full planned diffusion sampling algorithm, which can perform multiple iterations of planning and diffusion, can be seen in Algorithm
Algorithm 1 Planned Diffusion
1: function BOANNEDDDIFUSSINNES
2: loop
3:
Sample plan PAR(- e) Parse get K chunks .1....;50 8x with lengths 1,,...,4 l: = 1,..., ., do parallel
Autoregressively generate plan
Sample Inn ~ pm (: o(h))
Diffuse each chunk in parallel
7:
end for
(Fay...., Lox)
Concatenate chunks together Append plan and output to prior tokens
c (c. 2,
10:
=fnd = <eos/> then
11:
break
Final planning token triggers end of generation
12: end if
13:
end loop
14:
c- REMOVECCOTROOITAGS(()
D Strip planning tokens from final output
15:
return e
16:
end function
5

---

Published as a conference paper at ICLR 2026
4.2 IMPLEMENTATION
Control Tags. Chunks denoting groups of semantically related tokens are first defined during a sequential planning stage. This is done using a paired <topic?...<topic>>> tag structure. Within this structure, the model generates concise description of the chunk's s content (e.g., "definition" in Figure D and its predicted length (e.g., "30" in Figure D During the parallel diffusion stage, the model generates the tokens for each chunk within corresponding <async>.... "isync?...//asynn </async> tag pair. Finally, the <sync/> tag conveys generation dependency: tokens that follow <sync/> may require details produced inside the preceding <async> chunks, the sampling algorithm continues sequential planning only after those chunks are filled and available. We add all control tags to the model's vocabulary for training and inference and strip them during post- processing of final outputs.
Finetuning Dataset. We annotate the SlimOrca instruction-fiiettuiin dataset (Lian et al. 2023) for parallel generation, following Jin et al. (025). We prompt GEMINI model with the syntax and semanties of the control tags. See Appendix A for more details. GEMINI inserts the control tags into the response from each instruction-response pair in the dataset. The opening <async> carries two attributes: topic (a concise label, S 3 words) and tokens (a coarse length estimate, e.g., multiples of 10 tokens). We impose that every non-control-tag token lies inside exactly one <async>... ... </async> chunk (no nesting, no overlap). During preprocessing for training, we insert 0-10 tokens of stochastic padding inside each <async> chunk, allowing the diffusion model to generate with variable length shorter than the masked input. We validate well-formedness (balanced tags, coverage, non-overlap, attribute types/ranges) and discard malformed cases. Figure 3 a concrete example of the tagging language used by planned diffusion.
Training Loss.
The goal of training is to maximize the joint probability over plan tokens and their content. Given an annotated dataset D in which a single clean example is given by Y E ED, we decompose Y into sets of planning tokens Z and content tokens X, such that Y = 7 U X. X is noised sequence of tokens under noise distribution Cejo as previously defined. Thus, X contains masked tokens with probability t. Let fo be the function to instantiate planned diffusion, parameterized by 0. We use the notation fo (z, i) to signify that the model takes input x and makes a prediction at index of the sequence. Finally, let (X) be an attention masking function that takes as input a sequence X and outputs a subset Mx S X of the sequence that So has access to particular index i. We describe the attention masking in more detail in the following paragraph. With CE as the cross-entropy loss, our overall training objective is given by
[(0)= = E Y-D (0.1)
1(ys E Z))E(folVess f), vi) + 11 E X)CE((((M(XX)) U Z)) vi) (10) WEY
Σ
YY
Autoregressive
Diffusion
Note that in the training objective, the same noise parameterized by f is applied to chunks across multiple it- erations of planning and diffusion, where future diffusion chunks will be conditioned on previously sampled diffusion chunks. In this decision, we are applying the interpretation of a diffusion model as an any any-order autoregressive model capable of supporting arbitrary conditional queries at inference time (Shi al. (0224). The same technique is used by Llada 7B (Nie et al., (025), which is trained with a diffusion objective, but during inference used for eemi-autoregressive block sampling-
Attention Mask. We implement the following rules via the attention mask M, First, planning tokens, which are composed of control tags and their attributes, are given causal attention (Plan I and 2 in Figure 5). Second, diffused tokens use bidirectional attention, as required for -based parallel denoising (Diffusion and 2 in Figure 5).. In Section 5 we also show that restricting bidirectional dense attention within each chunk is another way to train planned diffusion. We refer to this variant as planned diffusion sparse attention (PSDA), which enforces full independence between chunks. The attention mask for PDSA can be seen in Appendix H
Variable Length Denoising, Typically, diffusion models are configured to generate given a fixed number of denoising steps. Generation quality increases and speed decreases with the number of steps. Unlike
6

---

Published as a conference paper at ICLR 2026
vanilla diffusion, planned diffusion does not generate a predetermined number of tokens, so the number of denoising steps cannot be fixed. We define a parameter r called the steps ratio. Given generation length |zl. the number of denoising steps will be = r * |a]. For a plan & and multiple diffusion chunks lengths l.....Ix. the step ratio is defined as = r * max; 4. The number of denoising steps depends only on the length of the longest chunk, because tokens in each chunk are denoised in parallel to other chunks. Higher step ratio corresponds to higher quality and slower generation, while lower step ratio leads to faster generation but worse quality.
5 EXPERIMENTAL EVALUATION
We experimentally assess the performance of planned diffusion, focusing on its trade-off between generation quality and latency. Our results show that planned diffusion expands the latency-qualins Pareto frontier for text generation when compared to autoregressive and other diffusion-based approaches. Furthermore, we demonstrate that our method scales better with additional compute; planned diffusion continues to improve with more training, whereas the performance of the autoregressive baseline plateaus.
Ye et al., 2025 Owen et al., 2025); the base model is first pre-trained autoregressively and then further pre- trained with diffusion objective. We train with A KKingma & Ba 2017: Loshchilov & Hutter 2019). peak learning rate 5 x 10-5 with linear decay, and 1 precision. We use per-GPU batch size I and global batch size 4. Because autoregressive and diffusion language models have different optimal epoch counts (Prabhudesai et al. 2025), we sweep epochs over {2, 4, 8, 16). We fine-tune on Gemini- annotated SlimOrea cestruction-folloiing data (see Section 4.2); we keep the control tags for planned diffusion and strip them for autoregressive and diffusion baselines, Training runs on 4 H200 (141 GB) with Py PyTorch (Imambi et al., 202T) and HuggingFace (Wolf et al.) 2020
Training setup. We fine-tune Dream-7B-Bat
Decoding strategies. We compare seven decoding strategies. (1) Autoregressive samples tokens sequentially from the autoregressive model. (2) Diffusion samples masked tokens in parallel from the diffusion model; we configure the number of denoising steps to equal the number new tokens as this produces the highest
quality generation (Lou et al., 2024 Shi al. 2025 Sahoo et al., 2024). (3) Fast-dLLM (Wu et al. 2025) samples from the same diffusion model but with an inference-time only optimization. We configure denois- ing steps to be half the number of new tokens and use confidence threshold of 0.9, which Wu et al. 2025 use as the default value. (4) Pasta-SFT (Jin et al. (025) utilizes control tags to implement semantic parallelism in a purely autoregressive setting. We compare the SFT-trained version of Pasta, as opposed to RL-trained, for fair comparison with planned diffusion, which is trained only with SFT. (5) Skeleton-of TfToougghh (Ning et al. 2024) autoregressively samples a bullet-point outline, then applies eegular-xpresssonn based syntactic pattern matching for extracting points for parallel expansion. (6) Planned diffusion (ours) samples a denois- ing plan autoregressively from the planned diffusion model, then samples masked tokens within each chunk in parallel from the same model; for each chunk, we configure denoising steps to equal its predicted token count. (7) Planned diffusion with sparse attention (ours) is a variant of planned diffusion. PDSA treat con- currently generated chunks fully independent conditioned on the plan, and as such, denoises the chunks using block-sparse attention (c.f., Figure (). By using a sparse attention mask, PDSA improves compute efficiency with sparse attention implementations but reduces model expressivity and GPU utilization from vanilla planned diffusion.
Inference setup. We sample with temperature 0.2 and top-p 0.95 following Yee et al. 2025), and cap the total sequence length at 2048 tokens. Inference runs on the same H200 hardware configuration.
Benchmark and metrics. We evaluate on AlpacaEval (805 instruction-follooiing prompts) (Li et al. 2023 Dubois et al. (024).. For each method we report: (i) average latency- -the mean wall-clock time per re- sponse; and (ii) weefiiyy--nnngthh-ootooooee win rate (LCWR) with an LLM-as-judge. We use the recom-
7

---

Published as a conference paper ICLR 2026
60
60
LC Win Rate (%)
LC Win Rate (%)
50
LC Win Rate (%)
50
40
+550
40
30
40-
400
10
10
20
Diffusion
F-dILLM
PD
PD-SA
AR
SaT
10
20
Diffusion
Pasta-SFT
PD
SoT
30
AR
-... PD
Pasta-SFT
Diffusion
PD-SA
50
100
150
500 1000 1500
2000
20
10
150
155
Latency (s)
Len Critical Path (steps)
Latency (s)
Figure 2: Evaluation of planned diffusion on the AlpacaEval benchmark. Left: A comparison of latency versus length-controlled win rate shows planned diffusion establishing a new Pareto frontier, offering a better trade-off between speed and quality. Middle: An analysis of the average critical path length reveals that planned diffuusio requires substantially fewer sequential forward passes than autoregressive generation. Right: A scaling analysis shows that planned diffusion's 's win rate continues to improve with more training, while the autoregressive baseline's ' performance flatlines. Within each method, color brightness from lightest darkest encodes 2, 4, 8, and 16 traing epochs. AR: Autoregressive; F-dLLM: Fast-dl.LM; SoT: Skeleton- of- Thought PD: Planned Diffusion; PDSA: Planned Diffusion Sparse Attention.
mended default configuration from (Dubois al. 2024) due its high correlation with human preference. We fix the LCWR reference to the best autoregressive baseline. We identify this reference by evaluating the autoregressive variants (2, 4, 8, and 16 epochs) against the 2-epoch variant and choosing the model with the highest quality | The 16-epoch model wins and serves as the fixed reference for all LCWR scores.
trade-off in the left figure of Figure 2 Planned diffusion (PD) achieves 22.4x speedup over Fast-dLLMM (F-dLLM) and higher quality (44.6% vs. 40.2% length- controlled win rate). Relative to autoregressive decoding (AR), planned diffusion achieves a 1.27x speedup while achieving 49.2% length-controlled win rate against the autoregressive reference (50.0%). Planned diffusion with sparse attention (PDSA) provides another point on the speed-quality Pareto frontier by fur- ther improving speedup to 1.81x relative to autoregressive decoding at 44.6% win rate. Diffusion attains the highest quality (52.6%) but requires substantially more inference time, requiring 25x the latency of au- toregressive decoding. Compared to other semantic parallelism methods, planned diffusion is firmly on the Pareto frontier. Skeleton-of-thougee
Quality and Latency. We plot the
offers marginally faster generations but incurs a significant decrease in accuracy. Pasta-SFT fails to properly learn the control tag semantics without additional RI training and produces worse quality and latency.
Speedup Analysis. We attribute much of planned diffusion's 's speedup over autoregressive decoding its shorter critical path of generation. We define critical path length as the number of forward passes required produce the final response. In dLLM, the critical path will be given by the number of denoising steps so the total complexity is the cost of attention multiplied by the number iterations: O(n² * *s), where is the sequence length. Planned diffusion reduces the number of steps s by factor of Imaz/ /n, where Imax the length of the longest chunk within a plan. The middle panel of Figure 2 shows that, on AlpacaEval, the average critical path of autoregressive decoding is 2.3x as long as that of planned diffusion (PD, 367.3 vs. 160.0 steps) and 2.8x as long the sparse attention variant (PDSA, 367.3 vs. 155.2 steps). This is expected, as planned diffusion enables multiple chunks to denoise simultaneously. The realized speedup (1.85x) smaller than the critical path reduction (2.8x for PD, 2.3x for PD-DA) because each planned diffusion step does more work: KV-cache reuse is lower and per-step compute is heavier than an autoregressive token step. We also observe a difference in the total number of tokens generated (this includes control tokens and They tie on length controlled win rate, so break ties using raw win rates.
8

---

Published as conference paper ICLR 2026
60
LC Win Rate (%)
40
LC Win Rate (%)
50
LC Win Rate (%)
40
40
30
30
20
20
Original
10
Ablate Topic
10
2.5
Ablate Sync
PO-SA
AR
10.
10
Latency (s)
Latency (s)
Latency (s, log scale)
Figure 3: Additional analysis. Left (Plan ablation):Removing topic from autoregressive plan harms quality yet removing <sync/> tokens boosts speedup without significantly hurting quality. Middle (chunk lengths): LCWR peaks a length-scaling factor of 1.0; length prediction autoregressive plan does not exhibit systemic error. Right (Quality versus Latency Sweep): Varying the step ratio = {0.25, 0.5, 0.75, 11) and the confidence threshold (r (0.4, 0.7, 0.8, 0.9)) hyperparame- ters produces smooth quality-latency trade-off.
pad tokens) between planned diffusion and autoregressive decoding. Planned diffusion and planned diffusion with sparse attention produce approximately 9.1 1% and 3.4% fewer tokens, respectively.
Scaling. The right panel of FigureEsshows how the latency-quality tradeoff evolves with training epochs for the four training configurations examine. Autoregressive training shows no benefit from additional train- ing compute: the length-conie length-controlled win rate remains flat at 50.0% across 2, 4, 8, and 16 epochs. Both variants of planned diffusion benefit moderately from additional training compute. Planned diffusion improves from 44.9% (2 epochs) to 49.2% (16 epochs), a gain of 4.3 percentage points, while planned diffusion with sparse attention improves from 40.2% (2 epochs) to 43.7% (16 epochs), gain of 3.5 percentage points. Diffusion benefits significantly from additional training compute, rising from 41.1 % (2 epochs) to 52.6% (16 epochs), a gain of 11.5 percentage points that ultimately surpasses the autoregressive baseline.
Takeaway. Planned diffusion sets a new latency-quality Pareto frontier and continuously improves with more training, while the autoregressive bascline plateaus.
ADDITIONAL ANALYSIS
We present additional analysis several design decisions key planned diffusion.
Denoising Plan Ablation. We remove two components of the autoregressive plan and evaluate their impact inference quality and latency. We train all model variants for 4 epochs in this study. The original 4-epoch planned diffusion achieves an LC win rate of 46.65% at 3.23 s latency. Removing the topic attribute from the training data and re-training the planned diffusion model significantly harms inference quality. Specifically, it reduces the length controlled win rate from 46.65% to 23.33% and the latency from 3.23 seconds 3.54 seconds. We conclude that topic attributes are critical for maintaining inference quality.
Recall that <sync/> marks synchronization barrier: decoding beyond it begins only after all prior content finalized. Deleting <sync/> tokens from the training data and re-training the planned diffusion model modestly reduces quality. Specifically, the LC win rate falls from 46.65% to 42.96% while the latency
"The gap in output length is in part due difference in optimal training epochs for planned diffusion (8ep) and autoregressive (16ep). The output length difference between 16ep planned diffusion and autoregressive reduces to 5.6%.

---

Published as a conference paper ICLR 2026
increases from 3.23
5.81 s. We conclude that omitting <syne/> slightly reduces quality but does not offer a latency benefit in this setting.
chunk Lengths. We test whether the model predicts chunk lengths accurately. Accurate length prediction is key to achieving good generation quality for planned diffusion, as the diffusion denoising phase of gener- ation cannot alter the chunk length. Systematic over-prediction wastes time by adding masks and denoising steps, while systematic under-prediction harms quality by forcing content truncation. To test for potential systematic deviation from the optimal generation length, we multiply the model's predicted chunk length by a length scaling factor to set the number of masks, sweeping the factor over {0.5, 1.0, 1.5, 2.0, 2.5).. We then measure length-controlled win rate (LCWR) and latency under identical inference settings. Latency rises with factors above 1.0 as expected because larger chunks require more mask tokens and denoising steps. Quality peaks at the model's originally predicted length (i.e scaling factor of 1.0). Deviating by =50% of the predicted length reduces LCWR. Interestingly, do not observe additional denoising steps lead- ing to accuracy improvements. The model's length predictions are accurate; we do not observe systematic wer/unddr-preedcctinn
Quality versus Latency Sweep. By default, to maximize generation quality, we set the number of denoising steps for chunk equal to that chunk's 's length, and we unmask a position only when the model is highly confidence of its prediction. In this analysis, we measure the quality-latency trade-off by varying the number of denoising steps as well as confidence threshold for decoding.
Planned diffusion employs hyperparameter called step ratio (see Section 4.2 which determines the number of denoising steps relative to the chunk length. The step ratio r scales steps relative to chunk length. When r equals 1 the number of steps equals the number of tokens; smaller r uses fewer steps per token. The confidence threshold r used by Wu et al. (2025) selects which positions to unmask at each denoising step. For each masked position, if the model's top-token probability that position is at least т, we decode that token and unmask the position; otherwise we keep it masked for decoding at a later time step.
Sweeping over {0.25, 0.5, 0.75, }} and over {0.4, 0.5, 0.6, 0.7, 0.8, 0.9} yields a smooth trade-off be- tween generation quality and inference latency; Across the step ratio and the confidence threshold, planned diffusion and its sparse attention variant yield higher quality at equal lower latency for most operating points.
Takeaway. Our analysis validates that our planning mechanism is minimal and reliable. At inference time, the step ratio and confidence threshold provide tunable control of the
trade-off.
7 CONCLUSION
In this work, we introduce planned diffusion, a novel hybrid architecture combining autoregressive planning with parallel diffusion- based execution to improve the trade-off between latency and quality in text gener- ation. Our model exploits opportunities for parallelism within the semantic structure of text. Experimental evaluation shows that planned diffusion expands the latency-quality Pareto frontier, achieving significant speedup over pure autoregressive and diffusion baselines with a minimal drop in quality. This efficiency gain driven by a shorter critical path and our approach's 's scalability with respect to training. We also show planned diffusion offers fine-graines control over the speed-quality trade-off at inference time. Planned diffusion provides a promising framework for developing faster and more efficient large language models.
10

---

Published as a conference paper ICLR 2026
ACKNOWLEDGEMENT
The authors acknowledge the MIT Office of Research Computing and Data for providing high performance computing resources that have contributed to the research results reported within this paper. This work was supported in part by the MIT-Google Program for Computing Innovation and by SRC JUMP 2.0 (CoCoSys). This material is based upon work supported in part by the National Science Foundation Graduate Research Fellowship under Grant No. 2141064. Any opinions, findings, and conclusions recommendations pressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work was funded in part by the DARPA ANSR, CODORD, and SAFRON pro- grams under awards 1-87750-23-2-0004 HRO01 12590089, and HR001 12530141, NSF grant IIS ISS194 1943641, and gifts from Adobe Research. Cisco Research, Qualcomm, and Amazon. This work was also supported by NSF CAREER Grant #234 1040, Schmidt Sciences Early Career Fellowship, Okawa Foundation Research Award. Approved for public release; distribution is unlimited.
REPRODUCIBILITY STATEMENT
We specify the starting checkpoint, fine- tne-tuning hyperparameters, inference settings, and hardware/software setup in Scction Section A includes a shortened version of the data-annotation prompt; we omit in-context examples for brevity.
LLM USE DISCLOSURE
We used LLMs to improve sentence-level writing and search for related work.
REFERENCES
Marianne Arriola, Aaron Gokaslan, Justin Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, and Volodymyr Kuleshov. Block diffusion: Interpolating between autoregressive and diffusion language models. arXiv preprint arXiv:2503.0957. 2025.
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured de- noising diffusion models in discrete state-spaces. Advances neural information processing systems, 34: 17981-17993, 2021.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training helpful and harmless assistant with rein- forcement learning from human feedback. arXiv preprint arXiv:2204. 05862, 2022.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pp. 1877-1901, 2020.
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean- -aan-ttptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling, 2023. URL //ttps://arxiv. org/abs//3022 01318
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03377 2021.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, lon Stoica, and Eric P. Xing. Vicuna: An open-source
11

---

Published as a conference paper ICLR 2026
chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023, URL https://Imsys.orgg// 1023-03-2 3i-vnca
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1-113, 2023.
Yann Dubois, Balázs Galambosi, Perey Liang, and Tatsunori B Hashimoto. simple way to debias automatic evaluators. arXiv preprint arXiv:2404.04475, 2024.
alpacaeval:
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. Koala: A dialogue model for academic research. Blog post, April 2023. URLhttps::/bairr berkeele.. /ttpg//2222//44////aaaa//
Feng Hong. Geng Yu, Yushi Ye, Haicheng Huang, Huangjic Zheng, Ya Zhang, Yanfeng Wang, and Jiangehao Yao. Wide-in, narrow-out: Revokable decoding for efficient and effective dllms. arXiv preprint arXiv:2507.18578, 2025.
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne den Berg, and Tim Salimans. Autoregressive diffusion models, 2022. URL
02037
Zhanqiu Hu, Jian Meng, Yash Akhauri, Mohamed S Abdelfattah, Jae-sun Seo, Zhiru Zhang, and Udit Gupta. Accelerating diffusion language model inference via efficient kv caching and guided diffusion. arXiv preprint urXiv:2505.2146) 2025.
Sagar Imambi, Kolla Bhanu Prakash, and GR Kanagachidammaresan. Pytorch. In Programming with Ten- sorFlow: solution for edge computing applications, pp. 87-104. Springer, 2021.
Daniel Israel, Guy Van den Broeck, and Aditya Grover. Accelerating diffusion Ilms via adaptive parallel decoding, arXiv preprint arXiv:2506.0044 2025a.
Daniel Israel, Aditya Grover, and Guy Van den Broeck. Enabling autoregressive models to fill in masked tokens. arXiv preprint arXiv:2502.06901, 2025b.
Tian Jin, Ellie Y Cheng, Zachary Ankner, Nikunj Saunshi, Blake M Elias, Amir Yazdanbakhsh, Jonathan Bagan-Kelley, Suvinay Subramanian, and Michael Carbin. Learning keep a promise: Scaling lan- guage model decoding parallelism with learned asynchronous decoding. In Forty-second International Conference on Machine Learning, 2025. URL httpshh:t////teetrre http://pwrreveeeeeeeee//r/////// net//frrumid777X444ZRR
Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham Kakade, and Sitan Chen. Train for the worst, plan for the best: Understanding token ordering in masked diffusions, 2025. URL htt.httts///arxv.ogr//b////
06768
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2017. URL https:
7/arxiv. org/ /bs/////..././////////////////// 1412. 6980
Andreas Köpf. Yannic Kilcher, Dimitri Von Rütte. Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Due Nguyen, Oliver Stanlcy, Richard Nagyfi, et al. Openassistant conversations- democratizing large language model alignment. Advances in neural information processing systems, 36: 47669-47681. 2023.
Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decod- ing, 2023. httpppp://aaxii.oorr///////1.11.
12

---

Published as a conference paper ICLR 2026
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al. Solving quantitative reasoning problems with language models. In Advances Neural Information Processing Systems, volume 35, pp- 3843-3857 2022.
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, 2025.
Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Alpacaeval: An automatic evaluator of instruction-following models. https: //github. com/ tatsu- hat.suu-aaaaappa_eeeeaa 5 2023.
Wing Lian, Guan Wang, Bleys Goodson, Eugene Pentland, Austin Cook, Chanvichet Vong, and "Teknium" Slimorca: An open dataset gpt-4 augmented flan reasoning traces, with verification, 2023. URL https:
//https://hugginggaacs http:///nnaa.....o//oooeeoeee co Orreeee-OrrrSSSmmmmmmmm
Anji Liu, Oliver Broadrick, Mathias Niepert, and Guy Van den Broeck. Discrete copula diffusion, 2025a.
URL
Jiaming Liu, Hao Chen, Pengju An, Zhuoyang Liu, Renrui Zhang, Chenyang Gu, Xiaoqi Li, Ziyu Guo, Sixiang Chen, Mengzhen Liu, et al. Hybridvla: Collaborative diffusion and autoregression in a unified vision-language-actinn model. arXiv preprint arXiv:2503. 10631, 2025b.
Mingdao Liu, Aohan Zeng, Bowen Wang, Peng Zhang, Jie Tang, and Yuxiao Dong. Apar: Llms can do 06761
auto-parallel auto-regressive decoding, 2024. URL
Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi Jaakkola, and Rafael Gómez- Bombarelli. Think while you generate: Discrete diffusion with planned denoising, 2025c. URL https: //arxiv org/ abs/ 2410. 06264
Zhiyuan Liu, Yicun Yang. Yaojie Zhang, Junjie Chen, Chang Zou, Qingyuan Wei, Shaobo Wang, and Lin- feng Zhang. dllm-cache: Accelerating diffusion large language models with adaptive caching. arXiv preprint arXiv:2506.06629 2025d.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. URL https://arxiv. org//bs//711..011
Aaron Lou, Chenlin Meng, and Stefano Ermon. Discrete diffusion modeling by estimating the ratios of the data distribution, 2024. http:/ttpss//aaii..rr/////////
16834
Xinyin Ma, Runpeng Yu, Gongfan Fang, and Xinchao Wang. dkv-cache: The cache for diffusion language models. arXiv preprint arXiv:2505. 15781, 2025.
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 arXiv:2502.00999. 2025.
Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, and Yu Wang. Skeleton-of-thought Prompting lims for efficient parallel generation, 2024. URL
Long Ouyang, Jeffrey Wu, Xu Jiang. Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, volume 35, pp. 27730-27744, 2022.
13

---

Published as a conference paper ICLR 2026
Jiayi Pan, Xiuyu Li, Long Lian, Charlie Snell, Yifei Zhou, Adam Yala, Trevor Darrell, Kurt Keutzer, and Alane Suhr. Learning adaptive parallel reasoning with language models, 2025. URL https://arxiv. org/abs/2504 15466
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean. Efficiently scaling transformer inference. Proceedings of machine learning and systems, 5:606-624 2023.
Mihir Prabhudesai, Mengning Wu, Amir Zadeh, Katerina Fragkiadaki, and Deepak Pathak. Diffusion beats autoregressive in data-constrained settings, 2025. URL httpt:/s///a:tt..ooo wwtpv//.rgg/bb///////// 15857
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, Yugiong Liu, Zeyu Cui. Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL httph:h//tatiiiiiee wtwgg//////////////////gg/ 2412.
Gleb Rodionov, Roman Garipov, Alina Shutova, George Yakushev, Erik Schultheis, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, and Dan Alistarh. Hogwild! inference: Parallel Ilm generation via concurrent attention, 2025. | httpthttss///rtrie www.aawwwww....r////reeee//////e 062611
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,
1301144 2024.
Subham Sekhar Sahoo, Zhihan Yang. Yash Akhauri, Johnna Liu, Deepansha Singh, Zhoujun Cheng, Zhengzhong Liu, Eric Xing, John Thickstun, and Arash Vahdat. Esoteric language models. arXiv preprint vrXiv:2506.01928, 2025.
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis Titsias. Simplified and generalized masked diffusion for discrete data. Advances in neural information processing systems, 37:103131- 103167, 2024.
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K. Titsias Simplified and generalized masked diffusion for discrete data, 2025. URL
04329
Mitchell Stern, William Chan, Jamie Kiros, and Jakob Uszkoreit. Insertion transformer: Flexible sequence generation via insertion operations, 2019. httl:ttts///ariii.oor///////////
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXhv:2302.13977, 2023.
Gilad Turok, Chris De Sa, and Volodymyr Kuleshov. Duel: Exact likelihood for masked diffusion via deterministic unmasking. arXiv preprint arXiv:2603.01367 2026.
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instructions. In Proceedings of the ōl annual meeting of the association for computational linguistics (volume 1: long papers), pp- 13484-13508, 2023.
Hao Wen, Yifan Su, Feifei Zhang, Yunxin Liu, Yunhao Liu, Ya-Qin Zhang, and Yuanchun Li. Parathinker: Native parallel thinking as a new paradigm to scale Ilm test-time compute, 2025. URL httpss///axvv.. org/abs/25099 04475
04475
14

---

Published as conference paper ICLR 2026
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Huggingface's 's transformers: State-of-the-an natural language process- ing, 2020. URL https://////r abs/1910. 03771
Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dllm: Training-free acceleration of diffusion Ilm by enabling kv cache and parallel decoding. arXiv preprint arXiv:2505.22615. 2025.
Xinyu Yang, Yuwei An, Hongyi Liu, Tianqi Chen, and Beidi Chen. Multiverse: Your language models secretly decide how parallelize and merge generation, 2025. URL http:///aarivv.oo/aa///////
09991
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 arXiv:2508. 15487, 2025.
Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, and Sharad Mehrotra. Draft&; ver- ify: Lossless large language model acceleration via self-speculative decoding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp- 11263-11282. Association for Computational Linguistics, 2024. doi: 1224
18653/v1/2024, URL http://dx wow.dx..oii.... 16..3//2/0.. acl-long.. 607
Chuyang Zhao, Yuxing Song, Wenhao Wang. Haocheng Feng, Errui Ding, Yifan Sun, Xinyan Xiao, and Jingdong Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint
arXiv2249..6688, 2024.
15

---

Published as conference paper ICLR 2026
A DATA ANNOTATION PROMPT
We present a shortened version of our data-annotation prompt below. We use it to instruct Gemini Flash 2.0 (temperature = 1.0, top-p = 0.95) to annotate our training data.
You will first identify whether the given chatbot response may be generated in parallel. You are to then annotate the chatbot response using specific tags that highlight segments suitable for parallel generation.
Use <async> tags to denote segments of text that may be generated asynchronously in parallel with respect to the text that follows. Thus apply <async> tags only to sentences that do not serve necessary context for subsequent sentences. Sentences that are crucial for understanding or generating following text are not sui table for parallel asynchronous generation. For each <async> tag, include a very concise topic description of the text surrounded within the <async> tags. The topic description will be accessible to text generation after the closing async tag to ensure continuity and coherence.
Use the singleton <sync/> tag for synchronization. All content generated before <sync/>, including text marked by <asyne> accessible to subsequent text generation after the <sync/> tag, ensuring continuity and coherence.
Detailed Instructions:
Tagging Rules:
Use <async> tag in pairs.
Ensure all text content is encapsulated within <async> tags.
Ensure that each <async> tag encompasses least five words.
Refrain from altering the content of the response during annotation.
Use maximum of words in the topic description.
Use <sync/> sparingly as it introduces significant slowdown.
B
TABLE SUMMARY OF EVALUATIONS
For completeness, we provide a table summary of the results in Figure 2
Table 1: Comparison of methods based on latency and length win rate.
Method
Latency (s)
LC Win Rate (%)
Diffusion
154.124
52.59%
AR
6.272
50.00%
Fast-dLLM Wu et al. (2025)
77.723
40.20%
Pasta-SFT (Jnn et al.
2025)
5,740
26.13%
Skeleton-of-thought (So1) (Ning et al.
2024
3.101
40.72%
Planned Diffusion (PD)
3,471
44.59%
Planned Diffusion Dense Attention (PD- (PDDDO)DITTDOO FIX]
4.934
49.17%
16

---

Published as conference paper ICLR 2026
C FULL SPEEDUP DISTRIBUTION
This section details the performance variance observed across the evaluation dataset. Figure 4 illustrates the distribution of speedup ratios obtained by comparing planned diffusion against the autoregressive baseline. The dataset consists of N 803 test samples. The analysis yields mean speedup ratio of 3.08 and the distribution exhibits a standard deviation 4.10. This significant variance relative to the mean suggests a right-skewed distribution, indicating that while the average performance gain is substantial, there exists subset cases where the proposed method achieves exceptionally high speedups.
No speedup (1.0x)
40
Median: 1.96x
Number of Instructions
30
20
10
2
6
(AR latency / PD latency)
Figure 4: Distribution of Speedup Ratios.
D KV CACHING IN PLANNED DIFFUSION
KV caching plays substantial role in the efficiency of planned diffusion. The KV cache of planned diffusion is derived from the model's 's hybrid attention mask. The general principle is that token can be cached if its key and value embeddings are unaffected by future tokens. This is determined by whether a token attends future positions in the sequence. The autoregressive planning stage uses causal attention and conventional application of KV caching (Pope et al. 2023). In contrast, the diffusion stage employs bidirectional mask, in which tokens inside <async> chunk attend to each other. Because bidirectional attention does not support KV caching (Israel et al. 2025b). tokens inside an <async> chunk cannot be cached until their respective denoising process is complete. However, subsequent tokens, such those in stage following <sync/> tag, can efficiently attend to the KV cache of the preceding planning and diffusion stages. This caching mechanism is essential for combining the speed of diffusion with the computational savings of autoregressive KV caching.
planning
E FURTHER DISCUSSION OF HYBRID MODELS
When referring to hybrid models between autoregression and discrete diffusion, we define a hybrid model as single architecture, capable of pure left-to-right autoregression and pure discrete diffusion. This definition
17

---

Published as conference paper ICLR 2026
precludes existing works that interpolate between the autoregressive and discrete diffusion objective, such as Block Diffusion (Arrioaa et al. 2025), Eso-LM (Sahoo et al. 2025), or ARDMs Hoogeboom et al. (2022). Another necessary clarification that discrete diffusion should be confused with vanilla continuous diffusion. For continuous diffusion, multimodal hybrids over autoregrssion and diffusion exist, such as HybridVLA (Liu et al. 2025b) and Monoformer ZZao et al. (2024).
F DATASET PERFORMANCE BREAKDOWN
The AlpacaEval dataset Li et al. 2023) composed of 5 datasets: self-instruct (Wang et al. 2023), open- assistant (Köpf et al. 2023), ) Vicuna (Chiang et al. 2023), koala (Geng et al. 2023), and hh-rthf (Bai et al. 2022). For planned diffusion, we report the length-controlll win rate for each and each standard emorr respectively. We observe that win rate is not especially sensitive to the dataset source of the instruction.
Table 2: Planned diffusion LC Win Rates across different datasets
Dataset
Number of Samples
LC Win Rate (% + SE)
self-instruct
249
45.7 + 0.65
open-assistant
187
44.2 = 0,91
vicuna
80
44.2 = 1.66
koala
156
48.0 = 0.57
hh-rlhf
129
46.3 = 1.04
G QUALITATIVE ANALYSIS
To better understand the behavior of Planned Diffusion, we analyze specific examples of generated outputs, focusing on the interaction between the autoregressive planning stage and the parallel diffusion stage. The examples below display the instruction and the raw output, which reveals the internal control tags generated by the planner.
In the raw output:
<promise>><topic>>... length</topich indicates the autoregressive plan for specific span.
<async>... </async> indicates the text generated parallel via discrete diffusion.
[PAD] tokens indicate the unused capacity of the allocated span length.
G.1 SUCCESSFUL EXAMPLE
The following example demonstrates a successful decomposition list-based query. The instruction asks for popular careers in development studies. The autoregressive planner successfully identifies distinct semantic entities before generation begins.
The planner generates specific, discriminatory topics for each span: UN orgs, WB orgs, NGOs, media orgs, etc. Because the planning stage explicitly disambiguates the content of each span, the subsequent diffusion execution stage, which generates these spans in parallel with independent attention masks, does not suffer from mode collapse. Each diffusion process conditioned on unique semantic target, resulting a comprehensive, non-repetitive list that correctly follows the instruction.
18

---

Published as conference paper ICLR 2026
Instruction
Please answer the following question based on the information provided in the article.
Development studies is an interdisciplinary branch of social science. Development studies is offered specialized master's degree number of reputed universities around the world. It has grown in popularity as subject of study since the early 1990s, and has been most widely taught and researched developing countries and countries with a colonial history, such as the UK, where the discipline originated. Students of development studies often choose careers in international organisations such as the United Nations, World Bank, non-government organisations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies and research centers.
What are some popular careers for students of development studies
Raw Output
wammmisse=ctopciinntrddeeee 2</topic>
<async>Some popular careers for students of development studies include: [PA]]]]]]][[]]]]P[[[[[]]]]]
<promise>~<toopcc>uu orgs 4</topic>
<promise>><topic>>NB orgs 4</topic>
<promisse>=<tooic>N000 4</topic>
<promiise>==coopicceedde orgs 4</topic>
<prommsse--tttppicddeellllpeeeee consultancy 4</topic>
<promise>>sstapicccSS bodies 4</tapic>
spromisse--topiicresseeee centers 4</topic>
<async>1. International Organizations: Students often work for organizations such the Uni ted Nations (UN) to address global issues, including poverty, education, healthcare, and education. TTt.:///DDAAAAAAAAAAAAAAAAAAAAAT
<async>2. World Bank (WB): Similar to the UN, the World Bank offers opportunities to work on projects related to development, finance, and infrastructure in various countries. [PAD][PAD] [PADICA00PPAQ]IPAIIRR////////
<async>3. Non-goverrmenttal Organizations (NGOs): : Many students of development studies pursue careers NGOs, as these organizations are often involved development projects and initiatives. Initiatives.[AAO]PP)) [PPPA//APATTTTTTTTTTAAAAAAAAAAAD
<async>4. Media and Journalism Houses: Students may become reporters and journalists, covering topics related to global issues, development, and the work of various organizations. the TTW.://AAAPAAITTTTTPTTTTTTAAAAAT
<async>5. Private sector development consultancy firms: Companies that provide advice and support to local businesses, helping them imprave their operations, strategies, and adapt to
<async>6. Corporate Social Responsibility (CSR) bodies: Students work in these bodies help businesses and organizations understand promote sustainable practices. [yaciiccs.[PAD][AAA]PPPP. AAA.D//AAADIAIIAPAAAAAAAAAAAAAAD <async>7. Research Centers: Students may also work in research centers, where they can contribute to the advancement of development studies research, analysis, and presentation.
meet their social responsibilities and
spromisee-<topicc>smmrr 4</topic>
<async>These are just a few examples of the many careers available to students of development studies. The field is diverse and offers tuniiten to contribute to the global advancement of development and social change.UUPo///PAAPPPPAAAAAAAA//
19

---

Published as conference paper ICLR 2026
Clean Output
Some popular careers for students of development studies include:
1. International Organizations: Students often work for organizations such as the United Nations (UN) address global issues, including poverty, education, healthcare, and education.
2. World Bank (WB): Similar to the UN, the World Bank offers opportunities to work on projects re- lated to development, finance, and infrastructure in various countries.
3. non-govermmental Organizations (NGOs): Many students of development studies pursue careers in NGOs, as these organizations are often involved in development projects and initiatives.
4. Media and Journalism Houses: Students may become reporters and journalists, covering topics lated to global issues, development, and the work of various organizations.
Private sector development consultancy firms: Companies that provide advice and support to local businesses, helping them improve their operations, strategies, and adapt to the market.
Corporate Social Responsibility (CSR) bodies: Students can work in these bodies help businesses and organizations understand and meet their social responsibilities and promote sustainable practices. Research Centers: Students may also work in research centers, where they can contribute to the advancement of development studies research, analysis, and presentation. These are just few exam- ples of the many careers available to students of development studies. The field is diverse and offers opportunities to contribute to the global advancement of development and social change.
G.2 FAILED EXAMPLE
The following example illustrates failure mode that can result from poor planning. In this case, the autore- gressive planner fails to decompose the semantic content effectively. Instead of generating specific locations (e.g.. "NYC", "Denver", "LA"), the planner generates generic structural tags: list part 1, list part list part
Instruction
Make list of the top places in the U.S. to visit at the given time.
November
20

---

Published as conference paper ICLR 2026
Raw Output
sprrmisse--stopiicnntrduuiii 2</topic>
spromise>>=<topic>lis prompt 1</topic>
<async>Sure, here's list of the top places in the U.S. to visit in Govember:<<asymne <async>Here are the top places to Wlst:::PADDPAA011ssyyxxxxxx
<promise>><ttopic>list part 4</topic>
auramisse><toopcc>list part 2 3</topic>
<promiise>=<<ooic>>list part 3 3</topic>
<promisse>=<toopcc>list part 3</topic>
<promisse>-<ttopic>>lss part 3</topic>
<prommise>-ctooiicllitt part 6 3</topic>
<promisee>-ctooicc>list part 7 3</topic>
<promisse>-stooic>llist part 3</topic>
<promisse>-sttooic>>litt part 3</topic> <promisee-<toopie>litt part 10 3</topic> <async>1. New York
<async>2. New York City - Lots of shopping, museums, and [aaertaammeenttpPppPPP00011AA [PAD]</async>
<async>2. New City It's a big city with lots of fun things, shops, and places to eat. New York City New York City is big city with lots of things to see, like the Statue of Liberty, the park, and the buildings. PPA]]PAA0JAAAOPPAA]PPAAAAAA00A0
<async>4. Denver, Colorado great place for football, hiking, and skiing. has the big city of Denver, the state Colorado, and the beautiful mountains around It's also famous for the Denver Zoo. [PA.PPIIAAAIPPAIPP11114////-
<async>5. Los Angeles, California Known for its beautiful coastline, Hollywood, and shopping, Los Angeles is top place to visit in November. Visit places like Santa Monica, Santa Barbara, and Santa Cruz, [AAD]PPO0[[PD][PPD]aasssss
enjoy the local food, beaches, and culture. [PAD
<async>8. Sacramento, California - Known for its rich history and beautiful coastline, Sacramento is major city in the state of California. It is the capital city of the United States and is home many major events and festivals, such the Sacramento Street Festival Sacramento Zoo, the Sacramento RIETRNKKKTAAAAAAAATTTAAAAAAAAAAA async>
<async>10. Denver, Colorado
Denver is known for its beautiful landscapes, history, and art scene. It's one of the most popular cities the U.S. and has lot do and see. Visit the U.S. Space Center, the Denver Zoo, and the University Colorado Boulder to get a taste <async>10. Denver, Colorado
(PAy.PPPD][PDD][PAN)
Denver is major city in the U.S. with diverse history, culture, and landscapes. It is home to numerous attractions, such the Denver Zoo, Science Center, andUSS, as well the University of Colorado Boulder and the Denver Zoo. The city is also known for its mountains, a
forests, and parks, making it great place for outdoor activities. [PADIPAA0[[PAO]]PA]]]AA))) [PAD][PA0]</async>
<async>10. Denver, Colorado
Denver is major city in the U.S. and offers mix of history, culture, and beauty. Some of the top places to visit in Denver include the U.S. Open Center, Central Park, the Denver Zoo, and the Denver Nature Center. The University of Colorado, also located in Denver, is great place explore, and the Denver Art Museum offers unique glimpse into the citD. [AAOI/AAAIPPPPIAAAAAAAAAAAAAAAAP
21

---

Published as conference paper ICLR 2026
Clean Output
Sure, here's a list of the top places in the U.S. to visit in November: Here are the top places visit:
1. New York City
New York City Lots of shopping, museums, and entertainment.
2. New York City It's big city with lots of fun things, shops, and places to eat.
3. New York City New York City is a big city with lots of things to see, like the Statue of Liberty, the park. and the buildings.
4. Denver, Colorado A great place for football, hiking, and skiing has the big city Denver, the state of Colorado, and the beautiful mountains around it. It's also famous for the Denver Zoo. 5. Los Angeles, California Known for its beautiful coastline, Hollywood, and shopping, Los Angeles is a top place visit in November. Visit places like Santa Monica, Santa Barbara, and Santa Cruz, and enjoy the local food, beaches, and culture.
8. Sacramento, California Known for its rich history and beautiful coastline, Sacramento city in the state of California. It is the capital city of the United States and is home to many major events and festivals, such as the Sacramento Street Festival, the Sacramento Zoo, and the Sacramento Riverwalk.
major
10. Denver, Colorado
Denver is known for its beautiful landscapes, history, and art scene. It's one of the most popular cities the U.S. and has a lot to do and see. Visit the U.S. Space Center, the Denver Zoo, and the University of Colorado Boulder to get taste of the city. 10. Denver, Colorado
Denver is major city in the U.S. with diverse history, culture, and landscapes. It home to numerous attractions, such as the Denver Zoo, Science Center, andUSS, as well as the University of Colorado Boulder and the Denver Zoo. The city is also known for its mountains, forests, and parks, making a great place for outdoor activities.
10. Denver, Colorado
Denver is major city in the U.S. and offers mix of history, culture, and beauty. Some of the top places visit in Denver include the U.S. Open Center, Central Park, the Denver Zoo, and the Denver Nature Center. The University of Colorado, also located in Denver, is great place to explore, and the Denver Art Museum offers unique glimpse into the city.
H PLANNED DIFFUSION ATTENTION MASKS
The attention mask for planned diffusion combines causal and bidirectional attention. Causal attention is used for sequential planning stages. Bidirectional attention is used within <async> chunks for parallel denoising. We enable concurrent chunks to cross-attend, unlike the sparse attention variant. After a <sync/> token, subsequent tokens can to attend to all prior tokens. For illustrative purposes, this example shortens the diffusion chunks.
planned diffusion sparse attention (SPDA), the asynchronous blocks cannot attend on another. Sparsity enables more compute efficient training and inference.
22

---

Published as conference paper ICLR 2026
0: <bos>
<tople>
Attended
task1
3:
Masked
Plan
4: </tapic>
<topic>
task?
</topic>
casyne>
10: A
Diffusion
</async>
13: <asynс>
14:
15:
</async>
17: <syne/>
18: <topic>
task3
20:
21: </topic>
22: <async>
Diffusion
24: </async>
23:
25: <eas>
1011
14 15
18 20 22 23 2425
Figure 5: Attention mask for planned diffusion.
0: <bas>
I: stopic>
Attended
3:
Masked
Plan
4: </topic>
5: <tapic>
6: task2
7:
8: </topic>
<async>
10:
11:
Diffusion
12: </async>
<async>
14:
15:
</asyn>>
17: <syne/>
18: stopic>
Plan
20:
21: </topic>
22: <async>
Diffusion 2
24: </async>
23:
25: <eos>
1011
15 16
18 19.19222222 24
Figure 6: Attention mask for planned diffusion with sparse attention (PDSA).
23


Reviews:
[{"review_id": "lk9gvh1A0g", "reviewer": "Reviewer_p8N9", "summary": "* Problem: Autoregressive models produce high-quality text but are slow because they generate tokens sequentially, while diffusion models generate in parallel but often require many iterative steps to achieve similar quality.\n* Solution: A new hybrid architecture, termed planned diffusion, that leverages (1) an autoregressive “planning” stage that decomposes the output into semantically independent spans, and (2) a parallel diffusion “execution” stage that denoises these spans simultaneously.\n* Evaluation: Experiments and analysis including demonstrating that the method achieves a 1.84x speedup over autoregressive generation with a 6.8% drop in win rate on AlpacaEval.", "questions": "1. The improvement in speed comes at a cost which is performance. Performance is a much harder thing to raise, so it is hard to understand exactly how much is being sacrificed for the speedup. Is there a way to quantify the speedup with the performance kept constant or to show the performance with the same speed?\n2. The following is mentioned: \"To the best of our knowledge, this is the first text-only model that uses both discrete diffusion and autoregression.\" Does the emphasis of \"text-only\" mean that there are other models in different modalities that use both discrete diffusion and autoregression?\n3. How does the model determine the optimal number and boundaries of spans in the autoregressive plan, and how sensitive are results to this segmentation?\n4. Could you clarify how the computational cost of the diffusion stage scales with the number of spans and denoising steps?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. Well-motivated: very relevant problem and one that addresses a key weakness in diffusion language models\n2. Novelty: combining an autoregressive planning stage with a diffusion-based parallel generation stage within a single unified model.\n3. Implementation: Proposes reasonable set of methods that includes a new control tag language, model training methodology, and inference algorithm that enable planned diffusion and navigation of a Pareto frontier between speed and performance.", "weaknesses": "1. Evaluation Scope: Evaluation is only on AlpacaEval and lacks any other benchmarks, tasks, or domains. \n2. Baselines: There is only one baseline that is not the vanilla baselines of autoregressive models and diffusion LLMs. \n3. Complexity: Quite a lot of complexity without full ablation to justify each design choice\n4. Trade-off: A performance loss of 6.8% is still pretty substantial and it is not clear how much speed-up one could get with say a smaller model or speculative decoding.", "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": "Z2XvxJ1a33", "reviewer": "Reviewer_UEZx", "summary": "The paper proposes Planned Diffusion, a hybrid text generation approach that first plans a response autoregressively (producing structure/length tags) and then fills multiple spans in parallel via discrete diffusion, aiming to shift the latency–quality Pareto frontier. On AlpacaEval, the method reportedly delivers ~1.8× speedup with a modest quality drop versus autoregressive decoding, and includes sensitivity analyses on span-length scaling and denoising step ratio.", "questions": "- Plan robustness: How often does the planner significantly under/over-estimate span length in the wild? Could a lightweight “repair” step (e.g., local AR patching) recover quality when spans are misplanned?\n\n- Generalization: Have you tried the approach on models with different pretraining (e.g., pure AR LLMs plus diffusion fine-tune) or at different scales?\n\n- Error modes: Is there any qualitative examples where planned diffusion fails (e.g., subtle cross-span dependencies) and discuss mitigation.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- Clear, appealing idea: formal two-stage factorization (planning then parallel diffusion), with an explicit algorithm and attention-masking design. \n- Well-specified control language (<topic>, <async>, <sync/>) that makes semantic parallelism concrete and implementable.\n- Empirical evidence of a new speed/quality trade-off vs. AR and diffusion baselines (latency–quality plots, critical-path analysis, scaling behavior).\n- Sensitivity analyses help demystify behavior: best performance when using the model’s predicted span lengths (scale=1.0) and a tunable quality–latency knob via step ratio.", "weaknesses": "- Benchmark scope: Results focus on AlpacaEval with an LLM-as-judge (LCWR). This is a useful proxy but not a robust test of coherence/faithfulness across diverse tasks (e.g., reasoning, long-form, safety). Lack of human evals or broader benchmarks (e.g., MT-Bench, GSM-8K reasoning slices, instruction-following suites) weakens generality.\n- Baselines & fairness details: Diffusion is configured with steps equal to token count, and fast-dLLM with specific hyperparameters; however, broader ablations (other drafting/verification, semi-AR methods, SoTA speculative decoding stacks) are limited. This makes it harder to judge the absolute Pareto gains.\n- Robustness & failure modes: The method assumes reliable plan quality (topic labels, span counts). What happens when planning is wrong (e.g., underestimates length; cross-span dependencies appear late)? The sensitivity section is a good start, but a qualitative error analysis is missing.\n- Training data annotation relies on a proprietary LLM (Gemini) to insert tags under constraints. Potential concerns: annotation consistency, domain shift to noisier instructions, and whether the model overfits the tag scheme rather than learning general “semantic parallelism.” More diagnostics would help.", "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": "SybpOiYc3f", "reviewer": "Reviewer_Zq7e", "summary": "This paper introduces planned diffusion, a hybrid text generation method combining autoregressive planning with diffusion-based parallel execution. The model first generates a structured plan with control tags defining independent text spans, then generates these spans simultaneously via discrete diffusion. Evaluated on AlpacaEval, the method achieves 1.84× speedup over autoregressive generation with a 6.8% drop in win rate, establishing a new point on the latency-quality Pareto frontier.", "questions": "1. How does planned diffusion compare quantitatively to other semantic parallelism methods?\n\n2. How does performance vary across task types beyond instruction-following (e.g., summarization, code generation, creative writing)?\n\n3. What is the speedup variance across examples? Are there cases where planning overhead makes it slower than baseline?\n\n4. What percentage of generations have poor plans? Can you provide failure case examples and error analysis?\n\n5. What content types decompose well vs. poorly? Does the method struggle with sequential reasoning or narratives?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. First text-only model combining discrete diffusion with autoregression in a unified architecture, addressing the speed-quality tradeoff from a novel angle\n2. Hybrid attention masking elegantly enables both causal and bidirectional attention; KV caching strategy is well-designed for this architecture\n3. Establishes new Pareto frontier point; sensitivity analysis confirms model learns accurate length prediction without systematic bias\n4. Method is orthogonal to other acceleration techniques and continues improving with more training data", "weaknesses": "1. Only AlpacaEval benchmark; no evaluation on diverse tasks (summarization, QA, code generation, creative writing). How does performance vary across task types?\n2. No direct comparison to other semantic parallelism methods (e.g., Skeleton-of-Thought, APAR, ParaThinker) despite extensive related work discussion. This is critical for establishing true contribution.\n3. Relies on Gemini for training data annotation. What is annotation quality? How many examples were rejected? Could this be learned end-to-end without synthetic supervision?", "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": "kfhU7n99xi", "reviewer": "Reviewer_Yrr4", "summary": "The paper proposes Planned Diffusion, a hybrid decoding framework that first generates a short autoregressive plan (with control tags like <async>…</async>, <sync/>, topic and predicted span length), then diffuses multiple spans in parallel under a bidirectional mask. It integrates KV-cache reuse across stages and uses an energy-ordered unmasking rule for diffusion. On AlpacaEval (805 prompts), it reports 1.27×–1.81× speedup over AR with 0.87%–5.4% LC win-rate drop, and shows ablations on plan components, span-length prediction, and quality–latency knobs (step-ratio r, confidence threshold τ).", "questions": "ref weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "# Strengths\n\n1) Clear plan-then-parallel idea  \n- First produce a short AR plan, then decode multiple spans in parallel with a bidirectional mask.  \n- Turns semantic parallelism into practical decoding parallelism.\n\n2) Simple, single-model pipeline  \n- Causal attention for planning; bidirectional attention within spans during diffusion.  \n- KV-cache reuse and stage transitions are straightforward to add to existing inference stacks.\n\n3) Transparent quality–latency control  \n- Tunable knobs (e.g., diffusion-step ratio *r*, confidence threshold *τ*) expose a smooth speed–quality trade-off.  \n- Pareto curves make the effect of these knobs easy to interpret.\n\n4) Useful diagnostics  \n- Component-wise ablations (control tags, span-length prediction, unmasking rule) clarify each module’s role.  \n- The explicit plan → execute interface helps surface failure cases.\n\n5) Reasonable reproducibility details  \n- Checkpoints, fine-tuning settings, and prompt snippets are documented sufficiently for re-implementation.", "weaknesses": "# Weaknesses\n\n1) Modest speedup and weaker quality\n- End-to-end acceleration is limited (≈ **1.27×**).\n- Reported quality can trail standard AR decoding (**49.2 vs. 50**).\n\n2) Reliance on prompt-model data construction\n- Plan annotations depend on a separate prompt model, introducing data-generation overhead and potential bias.\n- The dependence raises questions about scalability of the approach as model/data sizes grow.\n\n3) Narrow experimental scope\n- Evaluation is conducted on a **single benchmark**.\n- Lacks comparisons against a broader set of **model baselines** and decoding accelerators.\n\n4) Planning limitations on complex tasks\n- For tasks with strong cross-span dependencies, plan segmentation and span sizing are uncertain.\n- Observed speedup may be confounded by **implicit output-length constraints** rather than true parallelism.", "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)