# Dueling Bandits with Qualitative Feedback

arXiv: https://arxiv.org/abs/1809.05274 · HTML: https://mlsanity.com/paper/1809.05274/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Ordinal Monte Carlo Tree Search](https://mlsanity.com/paper/2101.10670/) (2021)
- [Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare](https://mlsanity.com/paper/2605.01961/) (2026)
- [Optimal Top-k Identification from Pairwise Comparisons](https://mlsanity.com/paper/2607.08979/) (2026)

## Abstract (arXiv)

We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulling each arm. We employ the same regret as the dueling bandit (DB) problem where the duel is carried out by comparing the qualitative feedback. Although we can naively use classic DB algorithms for solving the QDB problem, this reduction significantly worsens the performance---actually, in the QDB problem, the probability that one arm wins the duel over another arm can be directly estimated without carrying out actual duels. In this paper, we propose such direct algorithms for the QDB problem. Our theoretical analysis shows that the proposed algorithms significantly outperform DB algorithms by incorporating the qualitative feedback, and experimental results also demonstrate vast improvement over the existing DB algorithms.