OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering

Key points

• Successful trajectories may contain ineffective operations, while failed trajectories may contain useful ones.1
• To assess individual operations, we propose OpCredit, an operationaware policy-learning framework that combines downstream outcome credit with immediate transition credit: Branch Outcome Credit (BOC) reuses ordinary rollouts to compare terminal task rewards observed after different operations under matched navigation contexts; Frontier Transition Credit (FTC) measures operation-induced changes in answer-conditioned frontier potential.1
• The two complementary signals jointly supervise the corresponding decision spans during training, while inference uses only the learned policy and standard KG interface.1
• Experiments on ComplexWebQuestions, WebQSP, and GrailQA across three backbones demonstrate consistent improvements in KGQA performance.1

Sources (1)

• [1]OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 06:29 AM

  Agentic knowledge graph question answering(KGQA) relies on sequences of executable operations, but trajectory-level rewards obscure their individual contributions.

  Successful trajectories may contain ineffective operations, while failed trajectories may contain useful ones.

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