AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

• URL: http://arxiv.org/abs/2604.05846v1
• Date: Tue, 07 Apr 2026 13:11:17 GMT
• Title: AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
• Abstract summary: We introduce Agentic Graph Learning (AGL), a paradigm that reframes graph learning as an interleaved process of navigation and inference.<n>Specifically, we propose AgentGL, the first reinforcement learning (RL)-driven framework for AGL.<n>AgentGL substantially outperforms strong GraphLLMs and GraphRAG baselines.
• Score: 11.48092501779
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat external information as unstructured text and fail to leverage the topological dependencies inherent in real-world data. To bridge this gap, we introduce Agentic Graph Learning (AGL), a paradigm that reframes graph learning as an interleaved process of topology-aware navigation and LLM-based inference. Specifically, we propose AgentGL, the first reinforcement learning (RL)-driven framework for AGL. AgentGL equips an LLM agent with graph-native tools for multi-scale exploration, regulates tool usage via search-constrained thinking to balance accuracy and efficiency, and employs a graph-conditioned curriculum RL strategy to stabilize long-horizon policy learning without step-wise supervision. Across diverse Text-Attributed Graph (TAG) benchmarks and multiple LLM backbones, AgentGL substantially outperforms strong GraphLLMs and GraphRAG baselines, achieving absolute improvements of up to 17.5% in node classification and 28.4% in link prediction. These results demonstrate that AGL is a promising frontier for enabling LLMs to autonomously navigate and reason over complex relational environments. The code is publicly available at https://github.com/sunyuanfu/AgentGL.

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