APEX-Searcher: Augmenting LLMs' Search Capabilities through Agentic Planning and Execution

Researchers introduce APEX-Searcher, a new framework that enhances large language models' search capabilities through a two-stage approach combining reinforcement learning for strategic planning and supervised fine-tuning for execution. The system addresses limitations in multi-hop question answering by decoupling retrieval processes into planning and execution phases, showing significant improvements across multiple benchmarks.