SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought

📄 arXiv: 2505.24181v1 📥 PDF

作者: Guanghao Li, Wenhao Jiang, Mingfeng Chen, Yan Li, Hao Yu, Shuting Dong, Tao Ren, Ming Tang, Chun Yuan

分类: cs.AI

发布日期: 2025-05-30

💡 一句话要点

提出SCOUT框架以提升语言模型推理能力

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 链式思维 递归推理 语言模型 蒸馏训练 推理能力 深度学习 自然语言处理

📋 核心要点

  1. 现有的链式思维方法依赖于中间推理步骤，限制了模型的可扩展性和泛化能力。
  2. 本文提出流动链式思维（Flow CoT），通过将递归推理建模为潜在认知状态的渐进轨迹来解决这一问题。
  3. 实验结果显示，SCOUT在八个推理基准上提高了准确性和解释质量，微调下最高提升达1.8%。

📝 摘要（中文）

链式思维（CoT）提示通过鼓励逐步思考来改善大型语言模型（LLMs）的推理性能。然而，基于CoT的方法依赖于中间推理步骤，这限制了其可扩展性和泛化能力。近期研究探索了递归推理，LLMs在迭代中重用内部层以精炼潜在表示，但这些方法通常需要昂贵的预训练且缺乏系统框架。为此，本文提出了流动链式思维（Flow CoT），将递归推理建模为潜在认知状态的渐进轨迹。SCOUT（逐步认知优化使用教师）是一个轻量级微调框架，能够实现Flow CoT风格的推理而无需预训练。实验表明，SCOUT在八个推理基准上均提高了准确性和解释质量，验证了其有效性和Flow CoT的可扩展性。

🔬 方法详解

问题定义：本文旨在解决现有链式思维方法在推理过程中对中间步骤的依赖，导致的可扩展性和泛化能力不足的问题。

核心思路：提出流动链式思维（Flow CoT），将推理过程视为潜在认知状态的渐进演变，避免了对手动监督的依赖。

技术框架：SCOUT框架包含逐步蒸馏模块和基于交叉注意力的回顾模块，前者用于对齐每次迭代的教师模型，后者则整合前次迭代的输出，保持模型的原始计算流。

关键创新：SCOUT的主要创新在于其轻量级的微调方法，能够在没有预训练的情况下实现Flow CoT风格的推理，显著提升了推理的深度和质量。

关键设计：SCOUT采用逐步蒸馏对每次迭代进行优化，并设计了交叉注意力机制以整合历史信息，确保推理过程的连贯性和深度。具体的参数设置和损失函数设计在实验中进行了详细验证。

📊 实验亮点

实验结果表明，SCOUT在八个推理基准上均实现了准确性和解释质量的显著提升，微调下的最高提升达1.8%。这些结果验证了SCOUT的有效性，并展示了Flow CoT作为可扩展推理框架的实际可行性。

🎯 应用场景

该研究的潜在应用领域包括自然语言处理、智能问答系统和教育技术等。通过提升语言模型的推理能力，SCOUT可以在更复杂的任务中提供更准确的结果，未来可能对人机交互和自动化决策产生深远影响。

📄 摘要（原文）

Chain of Thought (CoT) prompting improves the reasoning performance of large language models (LLMs) by encouraging step by step thinking. However, CoT-based methods depend on intermediate reasoning steps, which limits scalability and generalization. Recent work explores recursive reasoning, where LLMs reuse internal layers across iterations to refine latent representations without explicit CoT supervision. While promising, these approaches often require costly pretraining and lack a principled framework for how reasoning should evolve across iterations. We address this gap by introducing Flow Chain of Thought (Flow CoT), a reasoning paradigm that models recursive inference as a progressive trajectory of latent cognitive states. Flow CoT frames each iteration as a distinct cognitive stage deepening reasoning across iterations without relying on manual supervision. To realize this, we propose SCOUT (Stepwise Cognitive Optimization Using Teachers), a lightweight fine tuning framework that enables Flow CoT style reasoning without the need for pretraining. SCOUT uses progressive distillation to align each iteration with a teacher of appropriate capacity, and a cross attention based retrospective module that integrates outputs from previous iterations while preserving the models original computation flow. Experiments across eight reasoning benchmarks show that SCOUT consistently improves both accuracy and explanation quality, achieving up to 1.8% gains under fine tuning. Qualitative analyses further reveal that SCOUT enables progressively deeper reasoning across iterations refining both belief formation and explanation granularity. These results not only validate the effectiveness of SCOUT, but also demonstrate the practical viability of Flow CoT as a scalable framework for enhancing reasoning in LLMs.