Core Insights - The article discusses the challenges faced by existing RL-driven deep thinking models, particularly the trade-off between accuracy and efficiency, where frequent calls to external search tools improve accuracy but significantly increase response time [2][6]. - The introduction of the LightSearcher framework by the Beijing University of Posts and Telecommunications AI team addresses these challenges by utilizing experiential memory and adaptive reward shaping to enhance efficiency while maintaining accuracy [3][9]. Summary by Sections Introduction - The need for deep thinking models to strategically control the use of search tools is emphasized, highlighting existing methods' shortcomings in balancing accuracy and efficiency [6]. LightSearcher Framework - LightSearcher is designed to optimize the use of search tools through experiential memory, which transforms implicit reasoning paths into explicit guiding experiences, and includes adaptive reward mechanisms [9][11]. Experimental Results - Comprehensive evaluations on multiple multi-hop QA benchmark datasets demonstrate that LightSearcher maintains competitive accuracy while significantly reducing search tool calls by 39.6%, reasoning time by 48.6%, and token consumption by 21.2% [18]. - The framework's core components include: - Contrastive Experiential Reasoning, which builds a dynamic memory library from high and low-quality reasoning paths [14]. - Adaptive Reward Shaping, which minimizes redundant tool calls and balances accuracy and efficiency [14]. - Experience-based RL training, which integrates accumulated experiences into prompt templates to guide efficient reasoning [14]. Conclusion - LightSearcher provides a new pathway for constructing efficient and reliable deep reasoning systems, with potential applications extending beyond multi-hop QA to areas like code synthesis and strategic planning [18][20].