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To address the challenge of process reward evaluation for large models in knowledge graph reasoning, this paper proposes the SCPRM model. By introducing schema distance and cumulative reward mechanisms, it effectively solves the risk compensation effect problem, achieving an average 1.18% improvement in Hits@k metrics on medical and legal knowledge graph question answering tasks.
Section 01
This paper proposes the SCPRM model to address the challenge of process reward evaluation for large models in knowledge graph reasoning. By introducing schema distance and cumulative reward mechanisms, it effectively solves the risk compensation effect problem, achieving an average 1.18% improvement in Hits@k metrics on medical and legal knowledge graph question answering tasks.
Section 02
In large model reasoning evaluation, traditional outcome reward models cannot guide intermediate steps; existing process reward models suffer from the risk compensation effect (incorrect intermediate steps still receive high rewards if corrected later). Knowledge Graph Question Answering (KGQA) has special challenges such as multi-path characteristics, high risk sensitivity (serious consequences of wrong paths in medical/legal fields), and schema constraints.
Section 03
SCPRM includes two key innovations: 1. Cumulative reward mechanism: Evaluates based on reasoning prefix conditions, considering coherence between steps and history; 2. Schema distance awareness: Measures the schema conformity between steps and the implicit target of the query, distinguishing between correct detours and wrong deviations. Integrate SCPRM into the Monte Carlo Tree Search (MCTS) framework to form the SCPRM-MCTS method to guide the search process.
Section 04
Evaluated on medical, legal KG datasets and the general CWQ dataset: The Hits@k metric improved by an average of 1.18%; it showed significant advantages in risk-sensitive reasoning scenarios, reducing the proportion of high-risk wrong steps and improving the reliability of practical applications.
Section 05
Technical contributions: Refined process reward evaluation, optimized reasoning using schema knowledge, and provided a path for risk-aware reinforcement learning. Insights: Building KGQA systems needs to emphasize the quality of reasoning paths; introducing process evaluation mechanisms in high-risk fields can improve credibility and practicality.
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