arXiv:2506.01250v3 Announce Type: replace Abstract: We introduce the first variance-aware algorithms for contextual dueling bandits that leverage shallow exploration strategies with neural networks for nonlinear utility approximation. A key theoretical challenge is the absence of a closed-form estimator, which led prior work to require an extremely large network width $m$ (i.e., $m = \widetilde{\Omega}(T^{14})$). We address this constraint with a novel analytical approach that combines iterative self-improvement with spectral analysis. Our analysis significantly reduces the network width requi
This paper addresses a key theoretical and computational challenge in advanced AI algorithms by demonstrating a significant reduction in required network width, making certain applications more feasible.
Advanced and more efficient AI algorithms, particularly in areas like reinforcement learning and contextual bandits, can underpin more sophisticated autonomous systems and decision-making processes.
The computational requirements for implementing variance-aware contextual dueling bandits with neural networks are significantly reduced, potentially broadening their applicability.
- · AI researchers
- · Reinforcement learning practitioners
- · Companies developing AI agents
- · Researchers reliant on computationally expensive previous methods
More efficient and effective AI models for online decision-making and learning become viable.
This efficiency could accelerate the development of more robust autonomous AI agents.
Improved agent performance could lead to faster adoption of AI in complex, real-world control systems requiring adaptive learning.
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