Source code of paper: Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
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Official repository for RAG-Gym
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.
A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines
R1-searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)
A modular graph-based Retrieval-Augmented Generation (RAG) system
