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Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning

Paper proposing a method for learning to reason by analogy using retrieval-augmented reinforcement fine-tuning.

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Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning

arXiv
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The paper presents a novel approach to analogical reasoning, combining retrieval-augmented models with reinforcement learning. The proposed method is evaluated on various tasks and demonstrates improved performance compared to existing methods.

This work contributes to the development of more efficient and effective analogical reasoning systems.

Abstract

The paper presents a novel approach to analogical reasoning, combining retrieval-augmented models with reinforcement learning. The proposed method is evaluated on various tasks and demonstrates improved performance compared to existing methods. This work contributes to the development of more efficient and effective analogical reasoning systems.

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analogical-reasoningreinforcement-learninglanguage-modelsknowledge-representationartificial-intelligenceLarge Language ModelsRetrieval & RAGSemantic InteroperabilityAI Agents
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