# A Framework for Fair Evaluation of Variance-Aware Bandit Algorithms

arXiv: https://arxiv.org/abs/2510.27001 · HTML: https://mlsanity.com/paper/2510.27001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates](https://mlsanity.com/paper/1711.03591/) (2017)

## Built on by (most cited first, 1 of 1)

- [Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search](https://mlsanity.com/paper/2512.21648/) (2025)

## Abstract (arXiv)

Multi-armed bandit (MAB) problems serve as a fundamental building block for more complex reinforcement learning algorithms. However, evaluating and comparing MAB algorithms remains challenging due to the lack of standardized conditions and replicability. This is particularly problematic for variance-aware extensions of classical methods like UCB, whose performance can heavily depend on the underlying environment. In this study, we address how performance differences between bandit algorithms can be reliably observed, and under what conditions variance-aware algorithms outperform classical ones. We present a reproducible evaluation designed to systematically compare eight classical and variance-aware MAB algorithms. The evaluation framework, implemented in our Bandit Playground codebase, features clearly defined experimental setups, multiple performance metrics (reward, regret, reward distribution, value-at-risk, and action optimality), and an interactive evaluation interface that supports consistent and transparent analysis. We show that variance-aware algorithms can offer advantages in settings with high uncertainty where the difficulty arises from subtle differences between arm rewards. In contrast, classical algorithms often perform equally well or better in more separable scenarios or if fine-tuned extensively. Our contributions are twofold: (1) a framework for systematic evaluation of MAB algorithms, and (2) insights into the conditions under which variance-aware approaches outperform their classical counterparts.