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This paper introduces GRiD, a framework that uses diffusion models and reinforcement learning to generate graph-like rules (e.g., cycles, branches) for knowledge graph reasoning, addressing the limitations of existing chain-rule mining methods. Experiments on six benchmarks show competitive performance in KG completion tasks.