arXiv Machine Learning October 9, 2026 By Kevin Riehl, Shaimaa K. El-Baklish, Fan Wu, Anastasios Kouvelas

Sample-Efficiency of Kolmogorov-Arnold Networks

Read the original on arXiv Machine Learning →

The paper investigates the sample‑efficiency of Kolmogorov‑Arnold Networks (KANs) in reinforcement learning. Using experiments on the Feynman dataset and Gymnasium benchmarks, the authors find that KANs achieve comparable performance to standard Multi‑Layer Perceptrons while requiring 40% fewer samples, with up to 50% relative performance gains during training. These improvements hold even when reward noise varies, underscoring KANs’ potential for more sample‑efficient RL.

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