From Search to Decision: How Post-Training Shapes Information-Seeking Capabilities in Large Language Models

Abstract

Information seeking in large language model (LLM) systems increasingly involves decisions about when to acquire information, where to search, and how to evaluate and use evidence. Recent post-training methods have improved several such behaviors, but their objectives and evaluations span different tasks and system settings. This paper organizes research around six interrelated information-seeking capabilities and examines the learning signals, optimization methods, update loci, and credit-assignment mechanisms used to develop them. We synthesize empirical progress in targeted behaviors, end-to-end performance, search control, and generalization. Existing studies report gains in retrieval routing, query refinement, multi-tool exploration, evidence-grounded reasoning, and resource-aware search, although the scope of behavioral evidence varies across capabilities. We identify open challenges in estimating prospective retrieval utility, coordinating information-seeking decisions, and adapting to unfamiliar retrieval environments, and discuss evaluation practices for interpreting future progress.