arXiv:2508.06165v5 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Learning from Verifiable Rewards (RLVR) for complex reasoning. However, existing attempts to unify these paradigms remain narrow in scope, typically limited to open-domain QA with fixed retrieval settings, which constrains generalization to broader domains. To address this limitation, we propose UR$^2$ (Unified RAG and Reasoning)), a general reinforcement learning frame
The continuous evolution of LLM capabilities and the desire to build more robust and generalizable AI systems are driving research into unifying complementary paradigms like RAG and RL.
This development proposes a more general framework for combining knowledge retrieval and complex reasoning in AI, potentially leading to more advanced and adaptable AI agents.
The scope of RAG and reasoning integration expands beyond narrow applications, enabling broader generalization across various domains for AI systems.
- · AI research labs
- · Developers of AI agents
- · Industries requiring complex reasoning in AI
Improved performance and versatility of large language models in complex tasks requiring both external knowledge and deductive reasoning.
Acceleration of the development of adaptable and autonomous AI agents capable of handling more diverse real-world problems.
New competitive landscape for AI platforms, where the ability to seamlessly integrate diverse AI capabilities becomes a key differentiator.
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Read at arXiv cs.CL