# Less Is More: Tuning Configurable Systems with Imperfect Fidelity

arXiv: https://arxiv.org/abs/2608.00759 · HTML: https://mlsanity.com/paper/2608.00759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 70 references that are in the map.
- First posted on arXiv 1 Aug 2026.

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

- [Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priors](https://mlsanity.com/paper/2602.00788/) (2026)

## Abstract (arXiv)

Configuration tuning is essential for optimizing the performance of highly configurable systems, e.g., throughput or runtime, under a given environment. Yet, this is a challenging process as there can be many options to tune, and configuration measurement is often highly expensive. In this paper, we demonstrate the phenomenon of ``less can be more'': system configuration tuning can be greatly improved with much superior budget utilization by partially tuning under the imperfect-fidelity---an environment that is similar, but cheaper to measure, compared with the concerned perfect-fidelity of environment under which the system should be tuned. We codify a conceptual framework of fidelity for configurable systems, drawing on which allows us to propose MFTune, a tuner that proactively explores in the space of >104 possible imperfect-fidelity settings to approximate a useful one, which strikes for the wideness of tuning. This creates high-quality seeds for the perfect-fidelity, which in turn ensures the tuning depth. Experiment results against 10 state-of-the-art tuners, obtained from running diverse real-world systems for 19 months 24 × 7, show that MFTune performs considerably better on 83.33% cases with up to 19.34% improvement while achieving hours of budget saving in general.