[Submitted on 7 Oct 2026]
Title:Sample-Efficiency of Kolmogorov-Arnold Networks
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Abstract:Deep reinforcement learning has achieved substantial performance gains over classical control approaches. Yet, a central challenge to learning in real-world applications is acquiring costly samples. Kolmogorov-Arnold Networks are a recently proposed architecture that can learn physical relationships in control problems effectively, with significantly higher parameter efficiency and interpretability when compared to Multi-Layer-Perceptron architectures. In this work, we systematically study sample-efficiency using computational experiments, covering the Feynman dataset and the Gymnasium RL benchmark. The results show that similar performance can be achieved with 40% fewer samples using the Kolmogorov-Arnold architecture, and that relative performance improvements up to 50% occur during the training process. The observed gains are robust to varying levels of noise in rewards. These results highlight the potential of the Kolmogorov-Arnold architectures for more sample-efficient reinforcement learning. Code: this https URL
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2610.10627 [cs.LG]
(or arXiv:2610.10627v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.10627
arXiv-issued DOI via DataCite
Submission history
From: Kevin Riehl [view email] [v1] Wed, 7 Oct 2026 11:17:37 UTC (357 KB)
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