AgentGL proposes a framework for agentic graph learning that combines large language models with reinforcement learning. The approach treats graph problem-solving as an agentic task, using RL to optimize LLM-based reasoning over graph structures. This bridges symbolic graph algorithms with neural language models.
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AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
AgentGL uses reinforcement learning to train LLMs as agentic solvers for graph problems, bridging symbolic graph algorithms with neural reasoning.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline
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