SEARCH-R1: Reinforcement Learning-Enhanced Multi-Turn Search and Reasoning for LLMs

The Research in discussion here introduces SEARCH-R1, a reinforcement learning (RL)-based framework that allows large language models (LLMs) to integrate multi-turn, interleaved search-and-reasoning capabilities. Unlike previous retrieval-augmented generation (RAG) or tool-use-based approaches, SEARCH-R1 trains LLMs to autonomously generate queries and optimize reasoning with search engine results using RL.

The key innovation is that the model learns entirely through reinforcement learning (without human-labeled trajectories) how to optimally perform search queries and reason through retrieved knowledge, significantly improving performance on question-answering tasks.

Motivation and Background

Source: Reference Section

Problem Addressed:

Large language models (LLMs) often face two major challenges:

• Complex reasoning: Even with chain-of-thought prompting, LLMs struggle with multi-step reasoning.
• Up-to-date, external knowledge: Relying solely on their parametric knowledge, LLMs can miss current or domain-specific information.

Prior Approaches:

• Retrieval-Augmented Generation (RAG): Combines retrieved documents with LLM prompts, but is…