Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning

ACL ARR 2026 January Submission925 Authors

Keywords: LLM Reasoning, Diffusion Language Model, Collaborative Reasoning

Abstract: Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their autoregressive generation paradigm makes it computationally expensive to explore diverse reasoning paths. In contrast, diffusion language models (DLMs) adopt a parallel, non-autoregressive generation mechanism that enables the efficient production of diverse candidate outputs. Motivated by this complementarity, we explore a collaborative reasoning framework that combines diffusion-based generation with autoregressive evaluation. Specifically, we leverage DLMs to efficiently generate diverse intermediate reasoning thoughts, and employ LLMs as evaluators to assess and select candidates based on their plausibility and correctness. By decoupling proposal generation from evaluation, our framework exploits the strengths of both models: efficient exploration from diffusion models and causally grounded assessment from autoregressive models, which naturally aligns with the divergent-convergent thinking framework in cognitive psychology. Experiments across various mathematical and logical reasoning benchmarks demonstrate that our framework improves inference efficiency while maintaining competitive or superior reasoning accuracy, laying the groundwork for building efficient reasoning architectures. Our code is open-source at https://anonymous.4open.science/r/Diffuse-Thinking-EC60 .

Paper Type: Long

Research Area: Mathematical, Symbolic, Neurosymbolic, and Logical Reasoning

Research Area Keywords: reasoning, LLM Efficiency, interactive and collaborative generation

Contribution Types: Model analysis & interpretability, Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 925