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Agentic Reinforcement Learning for Improving Knowledge Graph Question Answering Reliability

Large language models struggle with one-shot SPARQL generation for multi-hop knowledge graph questions, but training them as agentic systems with reinforcement learning enables reliable, iterative query refinement using execution feedback. A compact 3B-parameter model trained purely via outcome-driven RL learns to recover from errors and significantly outperforms zero-shot baselines, demonstrating a scalable blueprint for teaching AI agents to use formal symbolic tools effectively.