@inproceedings{lu-etal-2026-adaptive,
title = "Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning",
author = "Lu, Yi and
Kong, Deyang and
Wang, Jianing and
Guo, Linsen and
Wang, Xue and
Guo, Qi and
Gui, Tao and
Huang, Xuanjing and
Ye, Wei and
Zhang, Shikun and
Wang, Wei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1774/",
pages = "37952--37967",
ISBN = "979-8-89176-462-0",
abstract = "Recent advances in block diffusion language models have demonstrated competitive performance and strong scalability on reasoning tasks. However, their test-time compute allocation remains largely unexplored, leaving a critical speed-effectiveness trade-off unresolved in long Chain-of-Thought reasoning. To address this, we propose a unified test-time compute allocation framework that introduces adaptivity in both step-wise decoding and blockwise generation. At the decoding level, we propose Bounded Adaptive Confidence Decoding (BACD), a difficulty-aware sampling strategy that dynamically adjusts denoising based on model confidence, accelerating inference while controlling error accumulation. Beyond step-wise adaptivity, we introduce the Think Coarse, Critic Fine (TCCF) paradigm that allocates large block sizes for exploratory thinking and smaller block sizes for precise refinement. To stabilize training under varying block configurations, we adopt Progressive Block Size Extension, which mitigates quality degradation when scaling up block sizes. Extensive evaluations on six benchmarks show that our TDAR-8B model with BACD and TCCF achieves a 2.38 speedup and +3.4{\%} average accuracy over the strong TraDo-8B baseline, unlocking the potential of block diffusion in complex reasoning."
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<abstract>Recent advances in block diffusion language models have demonstrated competitive performance and strong scalability on reasoning tasks. However, their test-time compute allocation remains largely unexplored, leaving a critical speed-effectiveness trade-off unresolved in long Chain-of-Thought reasoning. To address this, we propose a unified test-time compute allocation framework that introduces adaptivity in both step-wise decoding and blockwise generation. At the decoding level, we propose Bounded Adaptive Confidence Decoding (BACD), a difficulty-aware sampling strategy that dynamically adjusts denoising based on model confidence, accelerating inference while controlling error accumulation. Beyond step-wise adaptivity, we introduce the Think Coarse, Critic Fine (TCCF) paradigm that allocates large block sizes for exploratory thinking and smaller block sizes for precise refinement. To stabilize training under varying block configurations, we adopt Progressive Block Size Extension, which mitigates quality degradation when scaling up block sizes. Extensive evaluations on six benchmarks show that our TDAR-8B model with BACD and TCCF achieves a 2.38 speedup and +3.4% average accuracy over the strong TraDo-8B baseline, unlocking the potential of block diffusion in complex reasoning.</abstract>
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%0 Conference Proceedings
%T Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning
%A Lu, Yi
%A Kong, Deyang
%A Wang, Jianing
%A Guo, Linsen
%A Wang, Xue
%A Guo, Qi
%A Gui, Tao
%A Huang, Xuanjing
%A Ye, Wei
%A Zhang, Shikun
%A Wang, Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lu-etal-2026-adaptive
%X Recent advances in block diffusion language models have demonstrated competitive performance and strong scalability on reasoning tasks. However, their test-time compute allocation remains largely unexplored, leaving a critical speed-effectiveness trade-off unresolved in long Chain-of-Thought reasoning. To address this, we propose a unified test-time compute allocation framework that introduces adaptivity in both step-wise decoding and blockwise generation. At the decoding level, we propose Bounded Adaptive Confidence Decoding (BACD), a difficulty-aware sampling strategy that dynamically adjusts denoising based on model confidence, accelerating inference while controlling error accumulation. Beyond step-wise adaptivity, we introduce the Think Coarse, Critic Fine (TCCF) paradigm that allocates large block sizes for exploratory thinking and smaller block sizes for precise refinement. To stabilize training under varying block configurations, we adopt Progressive Block Size Extension, which mitigates quality degradation when scaling up block sizes. Extensive evaluations on six benchmarks show that our TDAR-8B model with BACD and TCCF achieves a 2.38 speedup and +3.4% average accuracy over the strong TraDo-8B baseline, unlocking the potential of block diffusion in complex reasoning.
%U https://aclanthology.org/2026.findings-emnlp.1774/
%P 37952-37967
Markdown (Informal)
[Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning](https://aclanthology.org/2026.findings-emnlp.1774/) (Lu et al., Findings 2026)
ACL
- Yi Lu, Deyang Kong, Jianing Wang, Linsen Guo, Xue Wang, Qi Guo, Tao Gui, Xuanjing Huang, Wei Ye, Shikun Zhang, and Wei Wang. 2026. Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 37952–37967, Budapest, Hungary. Association for Computational Linguistics.