HiPRAG (Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation) is a reinforcement learning method designed for training reasoning-and-searching interleaved LLMs with improved efficiency and reduced oversearching as well as undersearching behavior.
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Latest capture 2026-09-07 03:02
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Source code of paper: Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Official repository for RAG-Gym
R1-searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
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[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank.
A High-Efficiency System of Large Language Model Based Search Agents
