Flow of Reasoning: Training LLMs for Divergent Problem Solving with Minimal Examples
Keywords: Large Language Models, reasoning, diversity, multi-step reasoning, planning, creativity
TL;DR: Flow of Reasoning (FoR) is a diversity-seeking finetuning method that enhances reasoning and creativity in large language models by using GFlowNets to discover diverse and accurate solutions through a Markovian flow on a directed acyclic graph.
Abstract: The ability to generate diverse solutions to a given problem is a hallmark of human creativity. This divergent reasoning is also crucial for machines, enhancing their robustness and enabling them to assist humans in many applications such as scientific discovery. However, existing approaches to multi-step reasoning with large language models (LLMs) have mostly focused only on the reasoning accuracy, without further discovering more diverse valid solutions. For example, supervised fine-tuning can improve LLM reasoning quality, but requires extensive supervised data to capture the full range of possible solutions. Reinforcement learning aims to find limited highest-reward solutions while neglecting the solution diversity. To fill this gap, we propose Flow of Reasoning (FoR), an efficient diversity-seeking LLM finetuning method aimed at improving reasoning quality and diversity with minimal data. FoR formulates multi-step LLM reasoning as a Markovian flow on a DAG-structured reasoning graph. This formulation allows us to incorporate and adapt principled GFlowNet approaches, for finetuning LLMs to sample diverse reasoning paths with probabilities proportional to the (unnormalized) reward of target problems. Extensive experiments show that, with limited training examples (e.g., 15 examples), FoR enables the discovery of diverse, creative, high-quality solutions, greatly outperforming a wide range of existing inference and training methods across five challenging puzzle-solving tasks, including BlocksWorld (embodied reasoning), Game24 (math puzzle solving), PrOntoQA (logical reasoning), Rubik's Cube (spatial reasoning), and 1D-ARC (abstraction reasoning).
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Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 8769
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