AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
The episode discusses 'AgenticRag-R1,' a framework for multi-step reasoning that addresses limitations in existing RAG systems. Hosts discuss how it integrates reasoning, retrieval, and memory using structured actions like `<backtrack>` to achieve robust and interpretable behavior over long tasks.