Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning

• URL: http://arxiv.org/abs/2509.20744v1
• Date: Thu, 25 Sep 2025 04:50:11 GMT
• Title: Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning
• Authors: Qihang Ai, Haiyun Jiang,
• Abstract summary: We study reasoning tasks through a framework that integrates auto-regressive (AR) and non-autoregressive (NAR) language models.<n>AR models, which generate text sequentially, excel at producing coherent outputs but often suffer from slow inference.<n>We introduce a new paradigm in which an NAR model efficiently produces intermediate reasoning traces, which subsequently guide an AR model to deliver precise final answers.
• Score: 14.161616899332993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study reasoning tasks through a framework that integrates auto-regressive (AR) and non-autoregressive (NAR) language models. AR models, which generate text sequentially, excel at producing coherent outputs but often suffer from slow inference, particularly in reasoning-intensive domains such as mathematics and code, where lengthy chains of thought are required. In contrast, NAR models, such as discrete diffusion models, allow parallel generation and offer substantial speedups, though typically at the cost of reduced output quality. To address these limitations, we introduce a new paradigm in which an NAR model efficiently produces intermediate reasoning traces, which subsequently guide an AR model to deliver precise final answers. Experiments demonstrate that our approach yields significant 26% improvements over strong baselines while substantially reducing inference cost.

Related papers

• Recursive Think-Answer Process for LLMs and VLMs [54.52289112197118]
  We propose an efficient Recursive Think-Answer Process (R-TAP)<n>R-TAP enables models to engage in iterative reasoning cycles and generate more accurate answers.<n>We show that R-TAP-enhanced models consistently outperform conventional single-pass methods.
  arXiv  Detail & Related papers  (2026-03-02T17:20:10Z)
• Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching [66.39914384073145]
  We propose a self-consistency framework that turns cheap diffusion-sampled reasoning into a reusable pool of step-level candidates.<n>We find that step-level recombination is most beneficial on harder problems.<n>Our training-free framework improves average accuracy by up to 2 across six math and coding tasks.
  arXiv  Detail & Related papers  (2026-02-26T11:08:39Z)
• Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models [29.56923793047279]
  We introduce Dynamic Outlier Truncation (DOT), a training-time intervention that selectively suppresses redundant tokens.<n>DOT targets only the extreme tail of response lengths within fully correct rollout groups while preserving long-horizon reasoning capabilities.<n>Our method reduces inference token usage by 78% while simultaneously increasing accuracy compared to the initial policy.
  arXiv  Detail & Related papers  (2026-01-07T14:31:07Z)
• Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models [49.598776427454176]
  Large Reasoning Models (LRMs) have gradually become a research hotspot due to their outstanding performance in handling complex tasks.<n>However, with the widespread application of these models, the problem of overthinking has gradually emerged.<n>Various efficient reasoning methods have been proposed, aiming to reduce the length of reasoning paths without compromising model performance and reasoning capability.
  arXiv  Detail & Related papers  (2025-08-04T06:54:31Z)
• Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling [12.835376812101323]
  We introduce the hypothesis that PRMs are also Partial Reward Models.<n>This allows for principled early rejection based on intermediate token-level signals.<n>On math reasoning benchmarks, our method achieves up to 1.4$times$-9$times$ reduction in inference FLOPs without degrading final performance.
  arXiv  Detail & Related papers  (2025-08-04T00:58:56Z)
• Inference Acceleration of Autoregressive Normalizing Flows by Selective Jacobi Decoding [12.338918067455436]
  Normalizing flows are promising generative models with advantages such as theoretical rigor, analytical log-likelihood, and end-to-end training.<n>Recent advancements utilize autoregressive modeling, significantly enhancing expressive power and generation quality.<n>We propose a selective Jacobi decoding (SeJD) strategy that accelerates autoregressive inference through parallel iterative optimization.
  arXiv  Detail & Related papers  (2025-05-30T16:53:15Z)
• Fast Autoregressive Models for Continuous Latent Generation [49.079819389916764]
  Autoregressive models have demonstrated remarkable success in sequential data generation, particularly in NLP.<n>Recent work, the masked autoregressive model (MAR) bypasses quantization by modeling per-token distributions in continuous spaces using a diffusion head.<n>We propose Fast AutoRegressive model (FAR), a novel framework that replaces MAR's diffusion head with a lightweight shortcut head.
  arXiv  Detail & Related papers  (2025-04-24T13:57:08Z)
• Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed [8.184624214651283]
  We propose a generic Guided Non-Autoregressive Knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances.
  arXiv  Detail & Related papers  (2023-12-19T07:13:32Z)
• Non-Autoregressive Machine Translation: It's Not as Fast as it Seems [84.47091735503979]
  We point out flaws in the evaluation methodology present in the literature on NAR models. We compare NAR models with other widely used methods for improving efficiency. We call for more realistic and extensive evaluation of NAR models in future work.
  arXiv  Detail & Related papers  (2022-05-04T09:30:17Z)
• A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation [59.64193903397301]
  Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines. We conduct a comparative study of various NAR modeling methods for end-to-end automatic speech recognition (ASR) The results on various tasks provide interesting findings for developing an understanding of NAR ASR, such as the accuracy-speed trade-off and robustness against long-form utterances.
  arXiv  Detail & Related papers  (2021-10-11T13:05:06Z)
• TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition [69.68154370877615]
  The non-autoregressive (NAR) models can get rid of the temporal dependency between the output tokens and predict the entire output tokens in at least one step. To address these two problems, we propose a new model named the two-step non-autoregressive transformer(TSNAT) The results show that the TSNAT can achieve a competitive performance with the AR model and outperform many complicated NAR models.
  arXiv  Detail & Related papers  (2021-04-04T02:34:55Z)
• An EM Approach to Non-autoregressive Conditional Sequence Generation [49.11858479436565]
  Autoregressive (AR) models have been the dominating approach to conditional sequence generation. Non-autoregressive (NAR) models have been recently proposed to reduce the latency by generating all output tokens in parallel. This paper proposes a new approach that jointly optimize both AR and NAR models in a unified Expectation-Maximization framework.
  arXiv  Detail & Related papers  (2020-06-29T20:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.