Knowledge-R1 is a framework designed to enhance the synergy between knowledge retrieval and reasoning capabilities. It addresses two fundamental challenges:
- Mitigating Knowledge Deficiency in Reason Models 🧐: Large reasoning models often lack sufficient knowledge to make informed decisions.
- Enhancing Reasoning in Adaptive Retrieval-Augmented Generation (RAG) Models 🔄📖: Traditional RAG models struggle with complex reasoning for improved query analysis, document analysis, and retrieval.
Knowledge-R1 introduces a novel agentic RAG reinforcement learning (RL) framework that enables multi-turn knowledge interaction. This approach:
- Enhances the model's ability to integrate retrieved knowledge into its reasoning process. 🏆
- Facilitates iterative refinement, allowing reasoning models to actively query and adapt retrieved knowledge. 🔄
- Optimizes knowledge-reasoning synergy through reinforcement learning. 🎯
The core methodology of Knowledge-R1 involves:
- Fast Agentic RAG Framework: Using batch inference to accelerate agentic RAG.
- Multi-Turn Knowledge Interaction 🔄🔍: Enabling stepwise retrieval and reasoning to progressively improve the model’s understanding and decision-making.
- Reinforcement Learning Optimization 🎯🔧: Employing reinforcement learning techniques to dynamically enhance the model's retrieval and reasoning alignment.
- ✅ Successfully reproduced results on Qwen-1.5B-Instruct, demonstrating significant improvements in knowledge reasoning tasks.
- ⚡ Partial implementation on 7B-scale models, though currently facing Out-Of-Memory (OOM) challenges.
We are still working on it! 😓💾
We have observed that the response length has been continuously increasing. However, as the length increases, we have encountered OOM (Out of Memory) issues. Consequently, training at the 7B scale has not yet been completed. We will continue to optimize.
Failed Result:
- Retriever: BM25s.
- Retrieval Corpus: Wiki2018.
- Dataset: 2wikimultihopqa
Hugging Face Dataset.
MIT