Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning

Understand

Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs) by integrating external knowledge and up-to-date information.

• However, traditional RAG systems are limited by static workflows and lack the adaptability required for multistep reasoning and complex task management.
• To address these limitations, agentic RAG systems (e.g., DeepResearch) have been proposed, enabling dynamic retrieval strategies, iterative context refinement, and adaptive workflows for handling complex search queries beyond the capabilities of conventional RAG.
• Recent advances, such as Search-R1, have demonstrated promising gains using outcome-based reinforcement learning, where the correctness of the final answer serves as the reward signal.