RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

Understand

Large Language Models (LLMs), despite their remarkable capabilities, are prone to generating hallucinated or outdated content due to their static internal knowledge.

• While Retrieval-Augmented Generation (RAG) integrated with Reinforcement Learning (RL) offers a solution, these methods are fundamentally constrained by a single-query mode, leading to prohibitive latency and inherent brittleness.
• To overcome these limitations, we introduce RAG-R1, a novel two-stage training framework centered around multi-query parallelism.
• Our framework enables LLMs to adaptively leverage internal and external knowledge during the reasoning process while transitioning from the single-query mode to multi-query parallelism.