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Cornell University
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LLM agents trained solely in rule-generated fictional worlds achieve state-of-the-art transfer to real-world search tasks, outperforming agents trained on actual data.
Forget expensive human annotations or hallucination-prone LLM-generated data: rule-generated synthetic data can teach LLMs to compose knowledge and significantly boost multi-hop reasoning on real-world tasks.
