arXiv:2605.25789v1 Announce Type: new Abstract: We study a stochastic multi-armed bandit problem where an agent is granted a free exploration budget before regret accumulates, a setting not captured by the classic regret minimization or pure exploration paradigms. The goal is to design an adaptive policy that strategically explores the bandit instance in the initial free exploration phase and minimizes the cumulative regret in the subsequent phase. We formalize this regret minimization with free exploration problem and identify an interesting regime where the free exploration budget scales log
This paper addresses a novel problem setting in multi-armed bandits, incorporating a 'free exploration budget,' which aligns with growing industry efforts to optimize AI agent learning and deployment efficiency.
Optimizing exploration strategies in AI systems, especially with early free exploration, can significantly reduce operational costs and improve performance, making AI applications more robust and efficient.
The formalization of 'regret minimization with free exploration' introduces a new facet to AI policy design, encouraging more strategic upfront data collection for deployed systems.
- · AI/ML researchers
- · Generative AI companies
- · Robotics developers
- · Optimization software providers
- · Inefficient AI deployment strategies
- · Brute-force exploration methods
More efficient and cost-effective deployment of AI agents that learn from interaction.
Accelerated development of autonomous AI systems with improved decision-making capabilities.
Enhanced AI system resilience and adaptability in complex, real-world environments with reduced resource expenditure.
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