Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets

• URL: http://arxiv.org/abs/2504.19981v1
• Date: Mon, 28 Apr 2025 16:56:41 GMT
• Title: Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets
• Authors: Adam Younsi, Abdalgader Abubaker, Mohamed El Amine Seddik, Hakim Hacid, Salem Lahlou,
• Abstract summary: We introduce a novel Process Reward Model (PRM) trained automatically using Monte Carlo Tree Search.<n>We then adapt Generative Flow Networks (GFlowNets) to operate at the reasoning step level.<n> Empirical evaluation shows strong improvements in both accuracy and solution diversity on challenging mathematical benchmarks.
• Score: 6.001837672951086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Achieving both accuracy and diverse reasoning remains challenging for Large Language Models (LLMs) in complex domains like mathematics. A key bottleneck is evaluating intermediate reasoning steps to guide generation without costly human annotations. To address this, we first introduce a novel Process Reward Model (PRM) trained automatically using Monte Carlo Tree Search coupled with a similarity-based data augmentation technique, effectively capturing step-level reasoning quality. Leveraging this PRM, we then adapt Generative Flow Networks (GFlowNets) to operate at the reasoning step level. Unlike traditional reinforcement learning focused on maximizing a single reward, GFlowNets naturally sample diverse, high-quality solutions proportional to their rewards, as measured by our PRM. Empirical evaluation shows strong improvements in both accuracy and solution diversity on challenging mathematical benchmarks (e.g., +2.59% absolute accuracy on MATH Level 5 for Llama3.2-3B), with effective generalization to unseen datasets (+9.4% absolute on SAT MATH). Our work demonstrates the potential of PRM-guided, step-level GFlowNets for developing more robust and versatile mathematical reasoning in LLMs.

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