Fine-tuning Timeseries Predictors Using Reinforcement Learning
Machine Learning
2026-03-23 v1 Artificial Intelligence
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.
Cite
@article{arxiv.2603.20063,
title = {Fine-tuning Timeseries Predictors Using Reinforcement Learning},
author = {Hugo Cazaux and Ralph Rudd and Hlynur Stefánsson and Sverrir Ólafsson and Eyjólfur Ingi Ásgeirsson},
journal= {arXiv preprint arXiv:2603.20063},
year = {2026}
}