Summary of Flow-dpo: Improving Llm Mathematical Reasoning Through Online Multi-agent Learning, by Yihe Deng et al.
Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning
by Yihe Deng, Paul Mineiro
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to generate detailed and accurate reasoning traces for Large Language Models (LLMs) fine-tuning. The proposed method, called online learning Flows, uses an incremental output production Flow where component LLMs collaboratively construct solutions through iterative communication. This approach is trained using online Direct Preference Optimization (DPO) learning with rollouts, generating DPO pairs for each training example and updating models in real-time. The quality of reasoning traces generated by this method is directly compared to those produced through direct model inference, demonstrating the effectiveness of the proposed approach in improving LLM performance in mathematical reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models (LLMs) think more clearly and logically about math problems. Currently, it’s hard for LLMs to explain how they came up with their answers. The researchers developed a new way to make LLMs generate detailed explanations of their thought processes. This approach is called online learning Flows, and it works by having multiple language models work together to solve a problem step-by-step. The method was tested and shown to improve the performance of LLMs in math problems. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Online learning » Optimization
