Fine-tuning Timeseries Predictors Using Reinforcement Learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| author | Cazaux, Hugo Rudd, Ralph Stefánsson, Hlynur Ólafsson, Sverrir Ásgeirsson, Eyjólfur Ingi |
| author_facet | Cazaux, Hugo Rudd, Ralph Stefánsson, Hlynur Ólafsson, Sverrir Ásgeirsson, Eyjólfur Ingi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20063 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fine-tuning Timeseries Predictors Using Reinforcement Learning Cazaux, Hugo Rudd, Ralph Stefánsson, Hlynur Ólafsson, Sverrir Ásgeirsson, Eyjólfur Ingi Machine Learning Artificial Intelligence 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. |
| title | Fine-tuning Timeseries Predictors Using Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.20063 |