This document introduces R1-Searcher++, a novel framework for Large Language Models (LLMs) designed to improve their ability to handle factual questions by strategically utilizing both their internal knowledge and external search capabilities. Unlike traditional methods that often over-rely on one source, R1-Searcher++ uses a two-stage training approach involving supervised fine-tuning followed by reinforcement learning. This allows LLMs to learn when to access external information and to incorporate retrieved information into their internal knowledge, leading to more efficient and accurate reasoning. The research demonstrates that this approach improves performance and reduces unnecessary external searches compared to existing techniques.

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