Meta-Learning for Autonomous Ai Agents: Enabling Self-Improvement Beyond Training Data
Abstract
This review explores the role of meta-learning in advancing autonomous AI agents capable of self-improvement beyond fixed training data. It examines core algorithms, theoretical models, and real-world applications to provide a comprehensive understanding of how meta-learning enables generalization and adaptation across tasks. The paper systematically categorizes meta-learning techniques into optimization-based, metric-based, model-based, and Bayesian approaches. It introduces formal mathematical models to define self-improvement using performance operators, convergence analysis, and regret minimization. Empirical benchmarks such as MiniImageNet, Omniglot, and MetaWorld are reviewed to illustrate performance trends. Meta-learning systems achieve high adaptability, with few-shot classification accuracies reaching 65–95% on standard benchmarks and up to 60% gains in sample efficiency for reinforcement learning agents. These systems facilitate rapid task adaptation, continual learning, and feedback-driven self-regulation, laying the foundation for strong autonomy in AI. The integration of meta-learners into robotics, NLP, vision, and human-in-the-loop systems demonstrates their potential for real-time, resilient intelligence in real-world environments. This review bridges theoretical insights with applied meta-learning, highlighting current limitations such as catastrophic forgetting and offering directions toward scalable, self-evolving AI agents.
References
[2] Peng, H. (2021). A brief survey of associations between meta-learning and general AI. arXiv preprint arXiv:2101.04283.
[3] Wodecki, A. (2020). Artificial intelligence in management: Self-learning and autonomous systems as key drivers of value creation. Edward Elgar Publishing.
[4] Gross, S. R. D. Revolutionizing Intelligence: Unraveling the Frontiers of Advanced Artificial Intelligence and its Impact on Society.
[5] Sharma, A. (2024). Reinforcement Learning for Autonomous Self-Improving Robotic Systems. Stanford University.
[6] Zhu, W., Wang, X., & Xie, P. (2022). Self-directed machine learning. AI Open, 3, 58-70.
[7] Turchin, A. (2018). Levels of Self-Improvement in AI and their Implications for AI Safety.
[8] Dafoe, A. (2018). AI governance: a research agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford.
[9] Waterworth, K. (2024). Model-Agents of Change: A Meta-Cognitive, Interdisciplinary, Self-Similar, Synergetic Approach to Neuro-Symbolic Semantic Search and Retrieval Augmented Generation (Master's thesis, Miami University).
[10] Peng, H. (2021). A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning. arXiv preprint arXiv:2103.00845.
[11] Gharehmohammadi, F. (2022). Foundation of Meta-Learning for Multi-Dimension Classification and Application (Doctoral dissertation, University of Georgia).
[12] Rosa, M., Afanasjeva, O., Andersson, S., Davidson, J., Guttenberg, N., Hlubuček, P., ... & Feyereisl, J. (2019). Badger: Learning to (learn [learning algorithms] through multi-agent communication). arXiv preprint arXiv:1912.01513.
[13] Thórisson, K. R., Bieger, J., Li, X., & Wang, P. (2019). Cumulative learning. In Artificial General Intelligence: 12th International Conference, AGI 2019, Proceedings 12 (pp. 198-208).
[14] Zhang, Y. Artificial Design: Modeling Artificial Super Intelligence with Extended General Relativity and Universal Darwinism via Geometrization for Universal Design Automation. In International Conference on Learning Representations.
[15] Wang, Y. X. (2018). Learning to learn for small sample visual recognition (Doctoral dissertation, Carnegie Mellon University).
[16] Mitsea, E., Drigas, A., & Skianis, C. (2024). Well-being technologies and positive psychology strategies for training metacognition, emotional intelligence and motivation meta-skills in clinical populations: a systematic review. Psych, 6(1), 305–344.
[17] REKHA, R. P. (2019). METACOGNITION, SELF-EFFICACY AND TEACHING COMPETENCY OF HIGH SCHOOL MATHEMATICS TEACHERS IN SOUTHERN DISTRICTS OF TAMIL NADU.
[18] Ray, A. (2018). Compassionate artificial intelligence: Frameworks and algorithms. Compassionate AI Lab.
[19] Yin, X., Wang, X., Pan, L., Wan, X., & Wang, W. Y. (2024). Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement. arXiv preprint arXiv:2410.04444.
[20] Setlur, A., Kang, K., Kumar, A., Behbahani, F., Raileanu, R., & Agarwal, R. Self-Improving Foundation Models Without Human Supervision. In ICLR 2025 Workshop Proposals.
[21] Zhu, D., Bu, Q., Zhu, Z., Zhang, Y., & Wang, Z. (2024). Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems. Frontiers in Neurorobotics, 18, 1385778.
[22] Sommer, A., Bazan, P., Fellerer, J., Babaeian, B., & German, R. (2025). Towards Adaptive Self-Improvement for Smarter Energy Systems. arXiv preprint arXiv:2501.19340.
[23] Gui, L. Y., Wang, Y. X., Ramanan, D., & Moura, J. M. (2018). Few-shot human motion prediction via meta-learning. In ECCV (pp. 432–450).
[24] Drexler, K. E. (2019). Reframing superintelligence: Comprehensive AI services as general intelligence.
[25] Israelsen, B. W. (2019). Algorithmic assurances and self-assessment of competency boundaries in autonomous systems (Doctoral dissertation, University of Colorado at Boulder).
[26] Gargano, J. P. (2019). Regret-based traces-exploration abstractions for large game solving. Artificial emotional intelligence integrates irrationality into moral rational agents.
[27] Seldon, A., & Abidoye, O. (2018). The fourth education revolution. Legend Press Ltd.
[28] Frost, D., Ball, S., Hill, V., & Lightfoot, S. Agents change
[29] Alqasim Shamshari1*, Habiba Najaf. (2021). MASTERING THE DATA UNIVERSE IN AI: BIG DATA'S POTENTIAL AND CHALLENGES. EPH -International Journal of Mathematics And Statistics. 7(2); DOI: 10.53555/eijms.v7i2.69
