mlsanityData to 7 Oct 2026

Fine-tuning Timeseries Predictors Using Reinforcement Learning

arXiv 2603.20063 ↗First posted 20 Mar 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 62 references that are in the map. First posted on arXiv 20 Mar 2026.

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References · 36

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24 more
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