Flow-DPO: Improving LLM Mathematical Reasoning through Online
  Multi-Agent Learning
Authors: name: Yihe Deng, name: Paul Mineiro

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.