- 🤖 Agentic RAG-R1: Enhance Agentic RAG Reasoning Capacity via Reinforcement Learning 🚀
Agentic RAG‑R1 is an open‑source initiative to build an Agentic Retrieval‑Augmented Generation (RAG) system by endowing a base language model with autonomous search & reasoning skills through reinforcement learning (currently using the GRPO algorithm).
Chinese Language Version:
English Language Version:
Agentic RAG combines two powerful concepts:
- Retrieval‑Augmented Generation (RAG): Combines generative power with on‑the‑fly retrieval from external knowledge bases, ensuring factual and up‑to‑date answers.
- Agentic AI: Gives the model the ability to decide when to retrieve, what to retrieve, and how to weave the retrieved evidence into its reasoning.
Our architecture is inspired by TC‑RAG and features an agent memory stack that orchestrates the full deliberation loop, supporting the following actions:
- Plan (❌)
- Reasoning (✅)
- Backtrack (✅)
- Summary (✅)
- Tool Observation – wiki/document/knowledge‑graph search, etc. (✅)
- Conclusion (✅)
Motivated by DeepSeek-R1, we apply GRPO (Generalized Relevance Policy Optimization) to reinforce the agent's choice of reasoning steps and retrieval actions, effectively boosting both search depth and answer quality.
We use conda to manage the environment. Follow these steps to set up:
conda create -n AgenticRAG python=3.11 -y
conda activate AgenticRAG
pip install -r requirements.txtWe provide our search tool repository ArtSearch as the search engine, which supports retrieval of information from Wikipedia. You can follow the instructions in that repository to deploy a local instance of the search system.
.
├── ArtSearch # Search tool integration
├── checkpoints # Model checkpoints
├── examples # Example use cases
├── experiments
│ ├── evaluation # Evaluation scripts and results
│ └── training # Training configurations
├── README.md
├── requirements.txt
├── script
│ ├── evaluation # Evaluation scripts
│ ├── run_server.sh # Server deployment script
│ └── training # Training scripts
├── service
│ ├── chat_client.py # Client for interacting with the model
│ └── chat_server.py # Server for hosting the model
├── src
│ ├── config # Configuration files
│ ├── data # Data processing utilities
│ ├── evaluation # Evaluation metrics and tools
│ ├── models # Model definitions
│ ├── train.py # Main training script
│ └── utils # Utility functions
Follow the steps below to get up and running with Agentic RAG‑R1.
Before you start, rename file ".env_format" to ".env" and fill the necessary os enviroment variables.
- Zero‑2 Mode
./script/training/train_zero2.sh
- Zero‑3 Mode
./script/training/train_zero3.sh
- Example Mode
comming soon~
- Server Mode
Launch the chat server:
./script/run_server.sh
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LoRA Tuning Support 🔧: Fine-tune efficiently with Low-Rank Adaptation
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Model Quant Support 💻: Support model quant to nf4 and ..
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Custom Agent Tools 🛠️: Integrate your own tools and personal RAG datasets
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Distributed Training 🌐: Support for Deepspeed Zero 2 Stage and Zero 3 Stage
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Efficient Resource Usage 💻: Support for models up to 32B parameters using only 2 A100 GPUs
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Tool Calling Reward 🎯: Enhanced reward model that includes:
- Accuracy reward
- Format reward
- RAG accuracy reward using the RAGAS framework
The total reward is calculated as:
$$r_{total} = r_{accuracy} + r_{format} + r_{rag}$$ -
TCRAG Integration 🔗: Use TCRAG as the rollout generator
We have made our training logs publicly available at: SwanLab Training Log
Our Qwen 2.5-7B-Instruct model was evaluated on the MedQA test set using Qwen‑2.5‑72B as the judge:
| Configuration | Format Accuracy | Answer Accuracy |
|---|---|---|
| Before fine-tuning | 39% | 84% |
| Before fine-tuning + search | 56% | 79% |
| After fine-tuning (200 steps) + search | 92% | 87% |
- Support PPO and SFT
- Add more tools
- [Additional planned features]
The concept of Agentic-RAG-R1 is inspired by Deepseek-R1 and TC-RAG. We sincerely appreciate the efforts of these teams for their contributions to open-source research and development. This work is in the same period as work with Search-R1 and ReSearch.
Supervisors: Junfeng Zhao, Xu Chu, Yasha Wang
Affiliation: Key Laboratory of High Confidence Software Technologies (Peking University), School of Computer Science, Peking University, China
If you use this work in your research, please cite:
@misc{Agentic_RAG_R1,
title = {Agentic RAG-R1: Enhance Agentic RAG Reasoning Capacity via Reinforcement Learning},
author = {Xinke Jiang, Jiaran Gao, Rihong Qiu, Wentao Zhang, Yue Fang, Hongxin Ding},
year = {2025},
howpublished= {\url{https://github.com/jiangxinke/Agentic-RAG-R1}},
note = {GitHub repository},
}This project is licensed under the Apache License. See the LICENSE file for details.