Reward on Path: Learning Intermediate Supervision Signals for Knowledge Graph Question Answering

arXiv:2605.10791v2 Announce Type: replace Abstract: Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent methods use supervision derived from answer labels or refined by Large Language Models (LLMs) to train models that retrieve KG evidence for LLM-based answer reasoning. However, answer-derived supervision treats every answer-reaching path as correct and thus yields noisy training signals, whereas LLM-refined supervision mitigates this noise at substantial cost. To address these limitations, we propose Reward on Path (RoP), a

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