SetCommit: Coordinated Token Commitments for Few-Step Diffusion Language Model Decoding

Yuannuo Feng; Yubing Ye; Wenshuai Yao; Yizhe Chen; Ngai Wong; Wenyong Zhou; Wang Kang

cs.AIOct 22, 2026

SetCommit: Coordinated Token Commitments for Few-Step Diffusion Language Model Decoding

Authors: Yuannuo Feng, Yubing Ye, Wenshuai Yao, Yizhe Chen, Ngai Wong, Wenyong Zhou, Wang Kang

Organizations: Beihang University · The University of Hong Kong · Peking University

Abstract

Diffusion language models generate multiple token positions in parallel, but few-step decoding makes parallel commitment difficult: tokens that appear reliable individually may be jointly inconsistent, while premature commitments can prevent later context-dependent refinement. In this paper, we introduce SetCommit, a training-free method for coordinated parallel token commitment. Instead of selecting tokens independently by confidence, SetCommit selects a commitment set that balances token reliability against local joint commitment risk, preserving conditioning opportunities for subsequent denoising steps. It further performs budget-neutral local exchanges, replacing vulnerable earlier commitments when newly revealed context supports more reliable alternatives. SetCommit requires no retraining or architectural modification and is compatible with existing diffusion decoding strategies. Experiments with LLaDA-8B and Dream-7B on GSM8K, MATH-500, MBPP, and HumanEval show accuracy/pass@1 gains of up to 7.4 percentage points over native decoding at 32 model evaluations. At common quality targets, geometric-mean incremental speedups range from $1.44$ to $1.74\times$ . Code is available at https://github.com/Kissmetothemoon/SetCommit.

Figures & tables

Figure 1: SetCommit complements system- and schedule-side diffusion decoding accelerators. Left: it coordinates parallel token commitments, orthogonally to reducing forward-pass computation or denoising updates. Right: predictions enter SetCommit ; the lower row’s selected positions (green) feed State Update under the host’s net quota. The return arrow follows the host scheduling rule.

Figure 2: Local clustering and context sensitivity on GSM8K. Left: counts of 16 top-

k

commitments per 10-position bin in one state. Center: cumulative excess adjacent pairs per prompt in the Discovery/Holdout subsets (240 prompts each). Right: mean absolute predicted-token posterior change after revealing a nearby prediction. Bands/bars are mean

\pm 1.96

SE; aggregation units and metrics are detailed in Appendix A .

Figure 3: Commitment structure and continuation accuracy. Left: 16 selected positions per rule in a shared state. Center: paired GSM8K accuracy differences from clustered top-

k

(pp), averaged over four source-progress anchors at 32 evaluations. Right: dispersed-minus-clustered gain versus source progress. Intervals are 95% paired bootstrap CIs over 240 prompts (Appendix A ).

Figure 4: Coordinated selection postpones a context-sensitive commitment. Conceptual word-level example with

q_{t}=4

and three baseline positions retained.

p

denotes posterior confidence and

s

the greedy score

g_{t}

. The first three rows share one forward pass; the last shows the next pass.

Method	GSM8K			MATH-500			MBPP			HumanEval			Mean extra
	$Q\uparrow$	NFE $\downarrow$	Total	$Q\uparrow$	NFE $\downarrow$	Total	$Q\uparrow$	NFE $\downarrow$	Total	$Q\uparrow$	NFE $\downarrow$	Total
LLaDA-8B Quality targets: 73.0/32.4/44.5/37.2%
Native	73.1	64	1.00	34.2	96	1.00	46.6	96	1.00	39.0	96	1.00	–
+ SetCommit	74.2	48	1.27	33.8	48	1.90	46.1	48	1.94	39.0	48	1.89	1.72
Fast fixed	73.1	64	2.01	34.0	96	1.96	44.7	64	2.93	39.6	96	1.91	–
+ SetCommit	74.6	48	2.38	34.6	48	3.72	46.8	48	3.47	38.4	48	3.36	1.47

Table 1: Multi-benchmark quality–cost comparison.

Q

: accuracy/pass@1 (%); NFE: mean evaluations. Total uses the matched-quality native host; mean extra averages paired-host speedups geometrically across tasks (3-point margin). Shading marks SetCommit; blue NFE marks paired reductions. Bold Total values are column-best within each model.

Figure 5: Paired quality–latency curves. LLaDA-8B/GSM8K on four hosts. Diamonds mark selected points; dashed lines mark the quality target. Arrows show latency savings; badges give paired NFE and incremental speedup, using the same records as Table 1 .

	LLaDA-8B		Dream-7B		Overhead
Variant	GSM8K	HumanEval	GSM8K	HumanEval	(%)
Host baseline	66.4	30.5	70.2	45.7	+0.0
Selection only	70.2	35.4	74.5	49.4	+1.7
Remask only	67.8	32.3	71.4	47.6	+1.3
Full SetCommit	71.4	37.2	76.0	50.6	+2.4
$\Delta$ vs. host (pp)	+5.0	+6.7	+5.8	+4.9	–

Table 2: Component and control ablations. Native hosts at 32 NFE. Quality is accuracy/pass@1 (%); bold marks column-best quality.

\Delta

gives gains over the host; overhead averages the latency change across the four settings.

Figure 6: Component contributions and controlled continuation. Left: native hosts at 32 NFE; columns show $Q_{10}-Q_{00}$ , $Q_{11}-Q_{10}$ , and $Q_{11}-Q_{10}-Q_{01}+Q_{00}$ (pp). L/D denote LLaDA-8B/Dream-7B; blue/amber indicate positive/negative values. Right: LLaDA-8B/GSM8K same-state selector gains over confidence-matched and stratified-random controls. Labels show differences from the matched control. All entries are point estimates.

Figure 7: Frozen configurations and deployment cost at 32 NFE. Left: frozen quality gains over the corresponding host (pp). Center: retuned minus frozen quality (pp); row order matches the left matrix. L/D denote LLaDA-8B/Dream-7B; 2.1/2.2 denote additional LLaDA targets. Color scales are separate and zero-anchored. Right: each point pairs quality gain and added latency for one primary model, task, and native/cached host; there are 16 points. Colors identify models and markers identify hosts in this panel.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8: Commitment distributions and paired outcome balance. Left and center: adjacent-pair rate and mutual-context exposure across 960 matched states per rule. Diamonds and values denote means; exposure uses

\log_{10}(1+x)

spacing with raw-unit ticks. Right: win/tie/loss shares for 240 GSM8K prompts after averaging each prompt’s paired score differences across progress anchors. Rows follow Figure 3 (center).

Set selection
Parameter	Native/Fast	EB
reliability	margin	posterior
use_dependency_risk	true	true
lambda_start/end	$0.03/0.01$	$0.03/0.01$
tau_start/end	$16/4$	$16/4$
shortlist_multiplier	$4.0$	$2.0$

Appendix

Table 3: Native/Fast and documented EB module configurations. Exchange ratios bound attempts, not observed edit frequencies.

Model	Role	Decoding route
LLaDA-8B	Primary	Uncached fixed; Fast fixed/adaptive; audited EB
Dream-7B-Instruct	Primary	Native entropy; Fast fixed/adaptive; separate EB runner
LLaDA2.1	Transfer	Model-specific decoding
LLaDA2.2	Transfer	Model-specific decoding

Appendix

Table 4: Model roles and evaluation routes. Transfer settings use model-specific decoding; Fast adapter coverage is stated for the two primary models. EB integration is documented separately.

Model	Task	Correct / total	$Q_{\mathrm{ref}}$ (%)	$q_{\star}$ (%)
LLaDA-8B	GSM8K	1003/1319	76.042456	73.042456
LLaDA-8B	MATH-500	177/500	35.400000	32.400000
LLaDA-8B	MBPP	203/427	47.540984	44.540984
LLaDA-8B	HumanEval	66/164	40.243902	37.243902
Dream-7B	GSM8K	1059/1319	80.288097	77.288097
Dream-7B	MATH-500	218/500	43.600000	40.600000

Appendix

Table 5: Shared native-128 quality references and three-point targets. Selection uses unrounded task scores.

Figure 9: Systems and robustness analyses. Top left: matched-quality LLaDA-8B/GSM8K runtime; module time includes signal handling, selection, exchange, and cache refresh. Top right: cumulative gains over each host for selection alone and selection plus exchange, on LLaDA-8B/GSM8K at 32 NFE. Bottom left:

100(L_{\mathrm{+SC}}/L_{\mathrm{host}}-1)

across generation lengths on the same model/task at 32 NFE. Bottom right: peak memory at length 1024: L/D denote LLaDA-8B/Dream-7B and N/C denote native/cached hosts. Labels above memory bars give added GiB.

Margin (pp)	Native + SetCommit	Fast fixed + SetCommit
1	1.68	1.54
2	1.85	1.71
3	1.73	1.48

Appendix

Table 6: Quality-margin sensitivity. Geometric-mean incremental speedup across two primary models and four tasks. Each target is reference quality minus the stated margin; operating points are reselected from the same sweep. Shading marks the main-table margin; entries are speedup factors.

LLaDA-8B
Benchmark	Native	Native + SetCommit		Fast fixed + SetCommit
	$Q_{128}$	$Q_{64}\uparrow$	Loss (%) $\downarrow$	$Q_{64}\uparrow$	Loss (%) $\downarrow$
GSM8K	76.0	76.0	0.00	76.0	0.00
MATH-500	35.4	34.6	2.26	35.2	0.56
MBPP	47.5	47.5	0.00	47.5	0.00
HumanEval	40.2	39.6	1.52	39.6	1.52

Appendix

Table 7: Quality retention at exactly 64 evaluations.

Q

is accuracy/pass@1 (%). The reference is native sampling at 128 NFE on the same model and task. Relative loss is

100(Q_{128}-Q_{64})/Q_{128}

, distinct from the 3-point selection margin in Table 1 . Teal losses are within 5%; bold

Q_{64}

values equal or exceed the reference. This same

Q_{128}

defines

Q_{\mathrm{ref}}

in Table 1 ; losses use unrounded scores.

Figure 10: Budget, continuation, and parameter analyses. Top left: GSM8K quality gains over native hosts at matched NFE. Top right: LLaDA-8B/GSM8K same-state continuation gains relative to the host at 32 NFE, before forming the control differences in Figure 6 . Bottom: independent relative risk-weight and remask-ratio-cap sweeps on LLaDA-8B/GSM8K at 32 NFE; all evaluated settings are shown with a common quality scale. These curves do not mark a selected default.

A. LLaDA-8B: all main-table configurations
Decoder	$Q$ (%)	Mean NFE	Total speedup ( $\times$ )
GSM8K ( $n=1319$ )
Native	$73.1\;[70.7,75.4]$	$64.0\;[64.0,64.0]$	$1.00\;[1.00,1.00]$
+ SetCommit	$74.2\;[71.8,76.6]$	$48.0\;[48.0,48.0]$	$1.27\;[1.25,1.29]$
Fast fixed	$73.1\;[70.7,75.4]$	$64.0\;[64.0,64.0]$	$2.01\;[1.96,2.06]$
+ SetCommit	$74.6\;[72.3,77.0]$	$48.0\;[48.0,48.0]$	$2.38\;[2.31,2.45]$

Appendix

Table 8: Uncertainty supplement to Table 1 . Entries: estimate [95% CI]; points are copied or derived from Table 1. Timing design: 3 warm-up and 10 paired measured rounds per setting. Timing CIs cover repeat variation only. Fixed-budget NFE and native self-ratios have degenerate intervals.

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Don't Commit Alone: Joint Token Commitment in Diffusion Large Language Models

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School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China · Zhongguancun Academy, Beijing, 100097, China

Diffusion Language Model Decoding Diffusion Language Models

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Diffusion large language models (dLLMs) commit multiple tokens per denoising step by decoding each selected position independently from the shared context; when those positions are dependent, the resulting factorization error is captured by conditional total correlation, which confidence-based selection cannot observe from marginals alone. We propose CoCommit, a marker-gated coordination pass that briefly defers commitment: after the usual bundle selection, a learned marker announces the commit set and the backbone's last-

n

layers are re-applied so marked positions coordinate -- approximating joint-mode decoding -- before greedy argmax writes tokens. The method reuses existing weights with one extra partial forward pass and no auxiliary model. On LLaDA2.1-mini with LoRA adapters and matched greedy inference, joint commitment improves accuracy on all six benchmarks we evaluate, with the largest gains on reasoning and exact-answer tasks.

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