Proceedings

Beyond Scaling: A Survey of Data-Efficient Learning for LLM Agents

Yaqing Wang, Zhenlin Luo, Peiyao Zhao, Yunfeng Cai, Quanming Yao

Download BibTeX Cite

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Survey Track. Pages 8050-8059.

https://doi.org/10.24963/ijcai.2026/893

Keywords

  • Machine Learning: Learnware/model reuse/transfer learning
  • Machine Learning: Few-shot learning
  • Agent-based and Multi-agent Systems: Applications

Abstract

LLM-based agents perceive, reason, act, and adapt through interaction. While scaling remains important, agentic progress also depends on extracting more learning signal from limited experience, including demonstrations, feedback, reasoning traces, tool-use records, and interaction trajectories. This survey develops an agent-centric view of data-efficient learning. We formulate an agentic learning loop, define data efficiency as capability improvement without proportional increases in supervision or real-environment trial-and-error, and synthesize methods into experience augmentation, agent structural design, and learning paradigms. We also summarize representative application domains and open challenges in experience reuse and cost-aware evaluation. Overall, the agentic era requires smarter ways to acquire, structure, reuse, and learn from limited experience.

Yaqing Wang, Zhenlin Luo, Peiyao Zhao, Yunfeng Cai, and Quanming Yao. 2026. Beyond Scaling: A Survey of Data-Efficient Learning for LLM Agents. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8050–8059. https://doi.org/10.24963/ijcai.2026/893
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards