title: Flow-DPO: Improving LLM Mathematical Reasoning through Online
    Multi-Agent Learning
summary: Mathematical reasoning is a crucial capability for Large Language Models
  (LLMs), yet generating detailed and accurate reasoning traces remains a
  significant challenge. This paper introduces a novel approach to produce
  high-quality reasoning traces for LLM fine-tuning using online learning
  Flows. Our method employs an incremental output production Flow, where
  component LLMs collaboratively construct solutions through iterative
  communication. We train the Flow using online Direct Preference Optimization
  (DPO) learning with rollouts, generating DPO pairs for each training example
  and updating models in real-time. We directly compare the quality of reasoning
  traces generated by our method with those produced through direct model
  inference, demonstrating the effectiveness of our approach in improving LLM
  performance in mathematical reasoning tasks.
paper_authors._id: 672196c94ed4ff86a1a2d479
paper_authors.name: Yihe Deng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:52:44.867Z
paper_authors._id: 642f4c789b2484d7d8551a93
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/642f4c789b2484d7d8551a93/0lH4YXcbZa-Xlzj6ESo7F.jpeg
paper_authors.fullname: Yihe Deng
paper_authors.type: user
paper_authors.user: ydeng9
paper_authors._id: 672196c94ed4ff86a1a2d47a
paper_authors.name: Paul Mineiro
paper_title: Flow-DPO: Improving LLM Mathematical Reasoning through Online
    Multi-Agent Learning
paper_summary: Mathematical reasoning is a crucial capability for Large Language Models
  (LLMs), yet generating detailed and accurate reasoning traces remains a
  significant challenge. This paper introduces a novel approach to produce
  high-quality reasoning traces for LLM fine-tuning using online learning
  Flows. Our method employs an incremental output production Flow, where
  component LLMs collaboratively construct solutions through iterative
  communication. We train the Flow using online Direct Preference Optimization
  (DPO) learning with rollouts, generating DPO pairs for each training example
  and updating models in real-time. We directly compare the quality of reasoning
  traces generated by our method with those produced through direct model
  inference, demonstrating the effectiveness of our approach in improving LLM
  performance in mathematical reasoning tasks.
paper_ai_summary: A novel approach using online learning Flows with Direct Preference Optimization enhances the quality of reasoning traces for LLMs in mathematical reasoning tasks.
paper_ai_keywords: Flows incremental output production Direct Preference Optimization (DPO) rollouts DPO pairs reasoning traces mathematical reasoning LLM fine-tuning