Zhenlin Luo

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 quick