# Sample-Efficiency of Kolmogorov-Arnold Networks

arXiv: https://arxiv.org/abs/2610.10627 · HTML: https://mlsanity.com/paper/2610.10627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Kolmogorov-Arnold Networks are Radial Basis Function Networks](https://mlsanity.com/paper/2405.06721/) (2024)
- [KAN 2.0: Kolmogorov-Arnold Networks Meet Science](https://mlsanity.com/paper/2408.10205/) (2024)
- [FC-KAN: Function Combinations in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2409.01763/) (2024)
- [Multifidelity Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2410.14764/) (2024)
- [KAN versus MLP on Irregular or Noisy Functions](https://mlsanity.com/paper/2408.07906/) (2024)
- [PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2501.07032/) (2025)
- [On Training of Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2411.05296/) (2024)
- [AF-KAN: Activation Function-Based Kolmogorov-Arnold Networks for Efficient Representation Learning](https://mlsanity.com/paper/2503.06112/) (2025)
- [From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation](https://mlsanity.com/paper/2501.09858/) (2025)
- [QuadKAN: KAN-Enhanced Quadruped Motion Control via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2508.19153/) (2025)
- [Comparative Evaluation of Kolmogorov-Arnold Autoencoders and Orthogonal Autoencoders for Fault Detection with Varying Training Set Sizes](https://mlsanity.com/paper/2508.02860/) (2025)
- [KAN-Dreamer: Benchmarking Kolmogorov-Arnold Networks as Function Approximators in World Models](https://mlsanity.com/paper/2512.07437/) (2025)

## Abstract (arXiv)

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: https://github.com/DerKevinRiehl/neurips26_kan_training