# Dominant Arm Identification with Mixing and Recycling Observed Samples

arXiv: https://arxiv.org/abs/2608.01545 · HTML: https://mlsanity.com/paper/2608.01545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Preference-based Online Learning with Dueling Bandits: A Survey](https://mlsanity.com/paper/1807.11398/) (2018)
- [Thompson Sampling Algorithms for Mean-Variance Bandits](https://mlsanity.com/paper/2002.00232/) (2020)
- [Best Arm Identification with Safety Constraints](https://mlsanity.com/paper/2111.12151/) (2021)
- [IntelligentPooling: Practical Thompson Sampling for mHealth](https://mlsanity.com/paper/2008.01571/) (2020)
- [Doubly robust Thompson sampling for linear payoffs](https://mlsanity.com/paper/2102.01229/) (2021)
- [Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure](https://mlsanity.com/paper/1604.05257/) (2016)
- [Quantile Bandits for Best Arms Identification](https://mlsanity.com/paper/2010.11568/) (2020)
- [Scalable Bid Landscape Forecasting in Real-time Bidding](https://mlsanity.com/paper/2001.06587/) (2020)
- [Learning the Pareto Front Using Bootstrapped Observation Samples](https://mlsanity.com/paper/2306.00096/) (2023)
- [Quantile Multi-Armed Bandits with 1-bit Feedback](https://mlsanity.com/paper/2502.06678/) (2025)

## Abstract (arXiv)

We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of exceeding the realized rewards of all other actions. Conventional mean-based and pairwise comparison-based algorithms often fail to identify the arm with the highest realized reward. To address this challenge, we introduce a novel dominant arm criterion and an efficient estimator with theoretical guarantees. Our approach relies on two key technical innovations: (i) a dominance score criterion that an arm beats the locally dominant over the partitioned reward space and (ii) a joint mixing and recycling mechanism coupled with a doubly robust estimator that guarantees simultaneous convergence of the empirical distribution functions for all arms. These key innovations pave a way to efficient computation of global arm dominance. Our proposed elimination algorithm identifies the best dominant arm with nearly optimal rate of sample complexity. Numerical experiments demonstrate that our algorithm consistently achieves exact recovery of the true dominant arm, outperforming existing baselines.