title: HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and
    Dynamic Workflows
summary: Despite recent advancements in large language models (LLMs), their
  performance on complex reasoning problems requiring multi-step thinking and
  combining various skills is still limited. To address this, we propose a novel
  framework HDFlow for complex reasoning with LLMs that combines fast and slow
  thinking modes in an adaptive manner. Our approach consists of two key
  components: 1) a new approach for slow, deliberate reasoning called Dynamic
  Workflow, which automatically decomposes complex problems into more manageable
  sub-tasks and dynamically designs a workflow to assemble specialized LLM or
  symbolic reasoning tools to solve sub-tasks; 2) Hybrid Thinking, a general
  framework that dynamically combines fast and slow thinking based on problem
  complexity. Finally, we propose an easy-to-scale method for automatically
  synthesizing a large-scale dataset of 27K challenging reasoning problems for
  complex reasoning and a hybrid thinking tuning method that trains smaller LLMs
  on this dataset to internalize the fast/slow hybrid reasoning strategies.
  Experiments on four reasoning benchmark datasets demonstrate that our slow
  thinking with dynamic workflows significantly outperforms Chain-of-Thought, and
  hybrid thinking achieves the highest accuracy while providing an effective
  balance between computational efficiency and performance. Fine-tuning using our
  hybrid thinking approach also significantly boosts the complex reasoning
  capabilities of open-source language models. The results showcase the promise
  of slow thinking, dynamic workflows, and hybrid thinking in expanding the
  frontier of complex problem-solving with LLMsCode and data will be
  released at \url{https://github.com/wenlinyao/HDFlow.}.
paper_authors._id: 66f6f2c386023f0d7df1dde1
paper_authors.name: Wenlin Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T07:16:06.884Z
paper_authors._id: 634f18e4aae4bde2c8e2adca
paper_authors.avatarUrl: /avatars/40549a59fc5ba04a4baa5a1d5dba0847.svg
paper_authors.fullname: Wenlin Yao
paper_authors.type: user
paper_authors.user: wenlinyao
paper_authors._id: 66f6f2c386023f0d7df1dde2
paper_authors.name: Haitao Mi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T07:16:13.365Z
paper_authors._id: 65147a1426fbd558dbd08f1b
paper_authors.avatarUrl: /avatars/86574ee2d5c22e940be1c4e50be88675.svg
paper_authors.fullname: Haitao Mi
paper_authors.type: user
paper_authors.user: haitaominlp
paper_authors._id: 66f6f2c386023f0d7df1dde3
paper_authors.name: Dong Yu
paper_title: HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and
    Dynamic Workflows
paper_summary: Despite recent advancements in large language models (LLMs), their
  performance on complex reasoning problems requiring multi-step thinking and
  combining various skills is still limited. To address this, we propose a novel
  framework HDFlow for complex reasoning with LLMs that combines fast and slow
  thinking modes in an adaptive manner. Our approach consists of two key
  components: 1) a new approach for slow, deliberate reasoning called Dynamic
  Workflow, which automatically decomposes complex problems into more manageable
  sub-tasks and dynamically designs a workflow to assemble specialized LLM or
  symbolic reasoning tools to solve sub-tasks; 2) Hybrid Thinking, a general
  framework that dynamically combines fast and slow thinking based on problem
  complexity. Finally, we propose an easy-to-scale method for automatically
  synthesizing a large-scale dataset of 27K challenging reasoning problems for
  complex reasoning and a hybrid thinking tuning method that trains smaller LLMs
  on this dataset to internalize the fast/slow hybrid reasoning strategies.
  Experiments on four reasoning benchmark datasets demonstrate that our slow
  thinking with dynamic workflows significantly outperforms Chain-of-Thought, and
  hybrid thinking achieves the highest accuracy while providing an effective
  balance between computational efficiency and performance. Fine-tuning using our
  hybrid thinking approach also significantly boosts the complex reasoning
  capabilities of open-source language models. The results showcase the promise
  of slow thinking, dynamic workflows, and hybrid thinking in expanding the
  frontier of complex problem-solving with LLMsCode and data will be
  released at \url{https://github.com/wenlinyao/HDFlow.}.
paper_ai_summary: A framework combining fast and slow thinking modes in LLMs for complex reasoning tasks outperforms existing methods and enhances computational efficiency in problem-solving.
paper_ai_keywords: large language models (LLMs) complex reasoning dynamic workflow Hybrid Thinking fast thinking slow thinking reasoning problems hybrid thinking tuning