Flow-DPO: Improving LLM Mathematical Reasoning through Online
  Multi-Agent Learning
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.
672196c94ed4ff86a1a2d479
Yihe Deng
claimed_verified
2024-10-30T09:52:44.867Z
642f4c789b2484d7d8551a93
https://cdn-avatars.huggingface.co/v1/production/uploads/642f4c789b2484d7d8551a93/0lH4YXcbZa-Xlzj6ESo7F.jpeg
Yihe Deng
user
ydeng9
672196c94ed4ff86a1a2d47a
Paul Mineiro
Flow-DPO: Improving LLM Mathematical Reasoning through Online
  Multi-Agent Learning
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.
A novel approach using online learning Flows with Direct Preference Optimization enhances the quality of reasoning traces for LLMs in mathematical reasoning tasks.
Flows incremental output production Direct Preference Optimization (DPO) rollouts DPO pairs reasoning traces mathematical reasoning LLM fine-tuning