[2602.23630] BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization

Computer Science > Machine Learning arXiv:2602.23630 (cs) [Submitted on 27 Feb 2026] Title:BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization Authors:Zhongyi Pei, Zhiyao Cen, Yipeng Huang, Chen Wang, Lin Liu, Philip Yu, Mingsheng Long View a PDF of the paper titled BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization, by Zhongyi Pei and 6 other authors View PDF HTML (experimental) Abstract:Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose \textbf{Bad Trial Tackler (BTTackler)}, a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculat...