Beyond Scaling: A Survey on Data-Efficient Agentic Learning

LLM-based agents are increasingly deployed across web and GUI automation, embodied decision making, and scientific workflows, yet their progress is often constrained by limited data and interaction. High-quality supervision is costly, and real-environment interactions are expensive, risky, and quickly invalidated by environment drift. This survey studies how to obtain and improve LLM-based agents with fewer samples, fewer labels, and fewer/ cheaper interactions. We view agentic learning as a closed-loop decision process where experience arises from both external supervision and online interactions, and data efficiency requires maximizing information yield per unit cost. We then introduce a unified agentic learning framework and organize the literature along three complementary dimensions: experience augmentation, agent structural design, and learning paradigms. This perspective connects design choices to where learning signals come from, how they are utilized, and how adaptation is performed under bounded budgets. We summarize representative benchmarks and synthesize key open challenges, aiming to clarify the emerging landscape and support future progress in data-efficient agentic learning.