[2603.20063] Fine-tuning Timeseries Predictors Using Reinforcement Learning

Computer Science > Machine Learning arXiv:2603.20063 (cs) [Submitted on 20 Mar 2026] Title:Fine-tuning Timeseries Predictors Using Reinforcement Learning Authors:Hugo Cazaux, Ralph Rudd, Hlynur Stefánsson, Sverrir Ólafsson, Eyjólfur Ingi Ásgeirsson View a PDF of the paper titled Fine-tuning Timeseries Predictors Using Reinforcement Learning, by Hugo Cazaux and Ralph Rudd and Hlynur Stef\'ansson and Sverrir \'Olafsson and Eyj\'olfur Ingi \'Asgeirsson View PDF Abstract:This chapter presents three major reinforcement learning algorithms used for fine-tuning financial forecasters. We propose a clear implementation plan for backpropagating the loss of a reinforcement learning task to a model trained using supervised learning, and compare the performance before and after the fine-tuning. We find an increase in performance after fine-tuning, and transfer learning properties to the models, indicating the benefits of fine-tuning. We also highlight the tuning process and empirical results for future implementation by practitioners. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.20063 [cs.LG]   (or arXiv:2603.20063v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.20063 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Eyjólfur Ingi Ásgeirsson [view email] [v1] Fri, 20 Mar 2026 15:44:40 UTC (191 KB) Full-text links: Access Paper: View a PDF of the paper titled Fine-tuning Timeseri...