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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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Official repository for RAG-Gym
Source code of paper: Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
R1-searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
No description.
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
A High-Efficiency System of Large Language Model Based Search Agents
