Fine-tuning Timeseries Predictors Using Reinforcement Learning

📰 ArXiv cs.AI

Fine-tuning timeseries predictors with reinforcement learning improves performance

advanced Published 23 Mar 2026

Action Steps

Implement reinforcement learning algorithms to fine-tune existing supervised learning models
Backpropagate the loss of the reinforcement learning task to the supervised learning model
Compare performance before and after fine-tuning to evaluate the benefits
Apply transfer learning properties to the fine-tuned models to adapt to new data

Who Needs to Know This

Data scientists and AI engineers can benefit from this approach to enhance the accuracy of their financial forecasting models, and product managers can leverage these improvements to inform business decisions

Key Insight

💡 Reinforcement learning can be used to fine-tune supervised learning models for timeseries prediction, leading to improved performance and transfer learning benefits

Key Takeaways

Fine-tuning timeseries predictors with reinforcement learning improves performance

Full Article

Title: Fine-tuning Timeseries Predictors Using Reinforcement Learning

Abstract:
arXiv:2603.20063v1 Announce Type: cross 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

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