Diffuse Thinking：探索扩散语言模型作为推理的高效思维提议器
Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning
AI总结：
针对LLMs自回归生成思维计算开销大且收益边际递减的问题，提出高效协作推理框架，利用扩散语言模型并行生成候选思维并由LLMs评估质量，在复杂推理任务中取得强劲表现。
摘要：
近年来，大型语言模型（LLMs）取得了显著进展，测试时缩放定律持续增强其推理能力。通过对各种中间思维进行系统评估和探索，LLMs展现出生成深思熟虑推理步骤的潜力，从而大幅提升推理准确率。然而，LLMs的自回归生成范式导致推理性能随测试时计算量的扩展呈次优状态，通常需要过多的计算开销来提出思维，却仅带来边际性能提升。相比之下，扩散语言模型（DLMs）可通过单次前向传播中的并行去噪高效生成多样样本，这启发我们利用其提出中间思维，从而在保持质量的同时减轻自回归生成带来的计算负担。在本工作中，我们提出了一种高效的协作推理框架，利用DLMs生成候选思维，并由LLMs评估其质量。在多个基准上的实验表明，该框架在复杂推理任务中取得了强劲表现，为未来研究提供了有前景的方向。代码开源于https://anonymous.4open.science/r/Diffuse-Thinking-EC60。
英文摘要：
In recent years, large language models (LLMs) have witnessed remarkable advancements, with the test-time scaling law consistently enhancing the reasoning capabilities. Through systematic evaluation and exploration of a diverse spectrum of intermediate thoughts, LLMs demonstrate the potential to generate deliberate reasoning steps, thereby substantially enhancing reasoning accuracy. However, LLMs' autoregressive generation paradigm results in reasoning performance scaling sub-optimally with test-time computation, often requiring excessive computational overhead to propose thoughts while yielding only marginal performance gains. In contrast, diffusion language models (DLMs) can efficiently produce diverse samples through parallel denoising in a single forward pass, inspiring us to leverage them for proposing intermediate thoughts, thereby alleviating the computational burden associated with autoregressive generation while maintaining quality. In this work, we propose an efficient collaborative reasoning framework, leveraging DLMs to generate candidate thoughts and LLMs to evaluate their quality. Experiments across diverse benchmarks demonstrate that our framework achieves strong performance in complex reasoning tasks, offering a promising direction for future research. Our code is open-source at https://anonymous.4open.science/r/Diffuse-Thinking-EC60.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
