Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning (2509.20744v1)

Abstract: We study reasoning tasks through a framework that integrates auto-regressive (AR) and non-autoregressive (NAR) LLMs. 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.

• The paper introduces a hybrid reasoning approach by integrating a fast, non-autoregressive model with a precise, auto-regressive system.
• It employs a two-stage inference pipeline where the NAR model generates concise reasoning traces that guide the AR model for detailed and accurate outputs.
• The system demonstrates up to a 40% improvement on mathematical benchmarks and a 26% overall efficiency gain, significantly reducing computational costs.

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

Introduction

The paper "Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning" (2509.20744) introduces a novel framework combining auto-regressive (AR) and non-autoregressive (NAR) LLMs to enhance efficiency in reasoning tasks. Traditional AR models, which generate sequences token-by-token, provide coherent outputs but suffer from slow inference, particularly in domains requiring complex reasoning like mathematics and programming. On the other hand, NAR models, such as those based on discrete diffusion processes, support parallel generation, thereby significantly reducing inference time; however, this often comes at the expense of output quality. This research proposes a hybrid system where an NAR model generates intermediate reasoning traces that guide an AR model to produce precise final responses, effectively achieving substantial performance improvements and reductions in computational costs.

Methodology

NAR and AR Integration

The integration of NAR and AR models is a critical element of the framework proposed in the paper. The NAR model's role is to efficiently produce compact and explicit reasoning traces, benefiting from its parallel generation capabilities and iterative refinement mechanisms. These traces then serve as a scaffold for the AR model, which excels in carrying out detailed and coherent reasoning resulting in accurate final outputs. This division of labor draws on the strength of each approach: the NAR model contributes swift inference and global context modeling, while the AR model handles the precision and fidelity required for intricate reasoning tasks.

Experimental Framework

The research builds upon various benchmarks, including mathematical problems and code generation tasks, to validate the effectiveness of the combined NAR and AR approach. A two-stage inference pipeline is utilized, where Mercury serves as the NAR reasoner producing succinct reasoning traces, and either Mercury itself (NAR→NAR) or GPT-5 (NAR→AR) carries out the final answer generation. The approach emphasizes minimizing computational overhead through effective reasoning trace generation without compromising output quality.

Results

The experimental results underscore the efficacy of the NAR+AR paradigm, with substantial improvements observed across diverse tasks. On competition-level mathematical benchmarks like AIME2025, the approach yields a 40% improvement, evidencing the high utility of compact plan generation in mitigating derivation errors. Similarly, notable enhancements are seen in code generation tasks, where structured, coherent planning from the NAR component facilitates precise AR execution.

The integration framework demonstrated an average improvement of 26% over the baseline. The results reflect a significant decrease in inference cost while achieving high accuracy across varying problem complexities.

Implications and Future Work

The hybrid framework newly established by the paper presents compelling implications for the development of efficient reasoning systems. By leveraging the complementary strengths of NAR and AR models, the paradigm enhances reasoning speed and quality, offering potential pathways for practical applications in rapid and accurate decision-making systems within AI-driven environments.

Future research directions should explore extending the framework to broader reasoning domains, evaluating the versatility and scalability of NAR-driven intermediary planning followed by AR execution. Additionally, exploring alternative NAR architectures with refined denoising capabilities could further amplify inference efficiency and output coherence, contributing to advancements in reasoning-intensive AI applications.

Conclusion

The paper "Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning" (2509.20744) effectively introduces a breakthrough methodology for improving reasoning efficiency in AI models through a strategic integration of NAR and AR systems. By harnessing the inherent advantages of parallel NAR generation and sequential AR precision, it presents a significant advancement in bridging LLM reasoning capability with practical inference demands.