# Trading off rewards and errors in multi-armed bandits

arXiv: https://arxiv.org/abs/2605.00488 · HTML: https://mlsanity.com/paper/2605.00488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 2 of 2)

- [Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits](https://mlsanity.com/paper/1507.04523/) (2015)
- [The Pareto Regret Frontier for Bandits](https://mlsanity.com/paper/1511.00048/) (2015)

## Abstract (arXiv)

In multi-armed bandits, the most-explored arms are the most informative, while reward maximization typically pulls only the best arm. We study the tradeoff between identifying arm means accurately and accumulating reward, and present an algorithm with regret guarantees that interpolates between the two objectives. We provide both upper and lower bounds and validate empirically.