Flow-guided Direct Preference Optimization for Knowledge Graph Reasoning with Trees

Recent advancements in knowledge graph question answering (KGQA) have shown promise, yet existing methods often fail to align with human reasoning patterns that involve continuous reflection and refinement. This paper proposes FD-PORT (flow-guided direct preference optimization for knowledge graph reasoning with trees), a novel approach that combines Monte Carlo Tree Search (MCTS) with flow-guided direct preference optimization (FDPO) for KGQA tasks. MCTS simulates human-like reasoning by systematically exploring multiple inference paths in knowledge graphs, while FDPO transforms the search feedback into fine-grained training signals through flow balance conditions. Unlike traditional methods focusing on end-to-end training or sequence-level preferences, FD-PORT establishes flow consistency between any states along the reasoning chain, enabling robust multi-hop reasoning that adapts to local decisions and long-range dependencies. Experimental results on three benchmark datasets demonstrate that FD-PORT significantly outperforms state-of-the-art methods, achieving up to 50.6% improvements over GPT-4 on complex multi-hop reasoning tasks with a smaller open-source language model. The framework is advanced in maintaining diverse reasoning paths while ensuring answer quality, closely mirroring human problem-solving strategies.