Learning to Refine: An Agentic RL Approach for Iterative SPARQL Query Construction

• URL: http://arxiv.org/abs/2511.11770v1
• Date: Fri, 14 Nov 2025 08:44:58 GMT
• Title: Learning to Refine: An Agentic RL Approach for Iterative SPARQL Query Construction
• Authors: Floris Vossebeld, Shenghui Wang,
• Abstract summary: Current methods lack the adaptive policies needed to dynamically debug queries based on real-time execution feedback.<n>This paper introduces a novel agentic framework where an LLM learns a resilient policy for the sequential process of iterative SPARQL construction.<n>We show that a compact 3B- parameter model, trained exclusively via outcome-driven Reinforcement Learning (GRPO), can learn effective policies for this task.
• Score: 0.18907108368038208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating complex, logically-sound SPARQL queries for multi-hop questions remains a critical bottleneck for Knowledge Graph Question Answering, as the brittle nature of one-shot generation by Large Language Models (LLMs) hinders reliable interaction with structured data. Current methods lack the adaptive policies needed to dynamically debug queries based on real-time execution feedback. This paper introduces a novel agentic framework where an LLM learns a resilient policy for the sequential process of iterative SPARQL construction. We show that a compact 3B-parameter model, trained exclusively via outcome-driven Reinforcement Learning (GRPO) without supervised fine-tuning, can learn effective policies for this task, discovering how to systematically recover from execution errors and refine its queries toward a correct answer. On a curated, executable single-answer subset of LC-QuAD 2.0, our agent achieves 49.7\% accuracy post-entity-linking, a significant 17.5 percentage point improvement over the strongest iterative zero-shot baseline. Further analysis reveals that while the agent's capability is driven by RL, its performance is enhanced by an explicit deliberative reasoning step that acts as a cognitive scaffold to improve policy precision. This work presents a generalizable blueprint for teaching agents to master formal, symbolic tools through interaction, bridging the gap between probabilistic LLMs and the structured world of Knowledge Graphs.

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