GraphDancer: Training LLMs to Explore and Reason over Graphs via Curriculum Reinforcement Learning

• URL: http://arxiv.org/abs/2602.02518v1
• Date: Sat, 24 Jan 2026 02:44:49 GMT
• Title: GraphDancer: Training LLMs to Explore and Reason over Graphs via Curriculum Reinforcement Learning
• Abstract summary: Large language models increasingly rely on external knowledge to improve factuality.<n>Many real-world knowledge sources are organized as heterogeneous graphs rather than plain text.<n>We propose GraphDancer, a framework that teaches LLMs to navigate graphs by interleaving reasoning and function execution.
• Score: 19.78190825157365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many real-world knowledge sources are organized as heterogeneous graphs rather than plain text. Reasoning over such graph-structured knowledge poses two key challenges: (1) navigating structured, schema-defined relations requires precise function calls rather than similarity-based retrieval, and (2) answering complex questions often demands multi-hop evidence aggregation through iterative information seeking. We propose GraphDancer, a reinforcement learning (RL) framework that teaches LLMs to navigate graphs by interleaving reasoning and function execution. To make RL effective for moderate-sized LLMs, we introduce a graph-aware curriculum that schedules training by the structural complexity of information-seeking trajectories using an easy-to-hard biased sampler. We evaluate GraphDancer on a multi-domain benchmark by training on one domain only and testing on unseen domains and out-of-distribution question types. Despite using only a 3B backbone, GraphDancer outperforms baselines equipped with either a 14B backbone or GPT-4o-mini, demonstrating robust cross-domain generalization of graph exploration and reasoning skills. Our code and models can be found at https://yuyangbai.com/graphdancer/ .

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