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.
Abstract (translated)
本章介绍了三种用于微调金融预测器的主要强化学习算法。我们提出了将强化学习任务的损失反向传播至经监督学习训练的模型的清晰实施方案，并比较了微调前后的性能表现。研究发现微调后性能有所提升，且模型展现出迁移学习特性，这表明微调具有显著效益。同时，我们强调了调优过程及实证结果，以供实践者未来实施参考。
URL
https://arxiv.org/abs/2603.20063
