通过结构化推理增强大模型，提升复杂任务表现
Enhancing Large Language Models through Structured Reasoning
- 将非结构化数据转为带推理步骤的结构化格式
- 在DeepSeek-R1-Distill-Qwen-1.5B上实现更简洁高效的推理
- 适合需要强逻辑与系统规划能力的研究者
近期大型语言模型（LLMs）在自然语言处理和自动化决策方面取得显著进展，但在涉及逻辑推理与系统规划的复杂任务中仍面临挑战，主要源于其依赖隐式统计关系而缺乏结构化知识表示。受认知科学与神经符号人工智能启发，我们提出一种通过显式结构化推理增强LLMs的新方法。首先，通过显式标注推理步骤，将非结构化数据转化为结构化格式；随后利用该结构化数据进行监督微调（SFT）。此外，采用组相对策略优化（GRPO），结合两种创新算法——最大流（MAX-Flow）与最长公共子序列（LCS），显著提升推理效率并降低计算复杂度。实验基于DeepSeek-R1-Distill-Qwen-1.5B模型，验证了其在多种场景下的简洁推理能力、鲁棒性能及对优化技术的良好兼容性，证明结构化推理集成的有效性。
原文摘要 · Abstract (English)
Recent Large Language Models (LLMs) have significantly advanced natural language processing and automated decision-making. However, these models still encounter difficulties when performing complex reasoning tasks involving logical deduction and systematic planning, primarily due to their reliance on implicit statistical relationships without structured knowledge representation.Inspired by cognitive science and neurosymbolic AI, we introduce a novel approach to enhance LLMs through explicit structured reasoning. First, we convert unstructured data into structured formats by explicitly annotating reasoning steps. We then employ this structured dataset to train LLMs through Supervised Fine-Tuning (SFT). Additionally, we enhance the structured reasoning capabilities of LLMs using Group Relative Policy Optimization (GRPO), incorporating two innovative algorithms--MAX-Flow and Longest Common Subsequence (LCS)--which notably improve reasoning effectiveness and reduce computational complexity. Experimental results from fine-tuning a DeepSeek-R1-Distill-Qwen-1.5B model demonstrate concise reasoning, robust performance across various scenarios, and improved compatibility with optimization techniques, validating the efficacy of structured reasoning integration in LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。