RANGER: Retrieval Agent for Navigating Graphs with Semantic Evaluation Rewards
The integration of Large Language Models and Reinforcement Learning offers a promising direction for complex sequential decision-making tasks, such as multi-hop question answering over knowledge graphs. While LLMs provide strong semantic understanding and reasoning capabilities, they often struggle with precise, multi-step navigation in structured environments. Conversely, RL agents excel at learning navigation policies but are hindered by the difficulty of designing semantically meaningful reward functions. This paper introduces a novel hybrid approach in which an LLM serves as a mentor to an RL agent during training by providing dense, semantically informed reward signals. The RL agent,...
| Authors | Artem Mikhaylov, Pavel Konoplev, Nikolay Efanov et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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