Fine-tuning Timeseries Predictors Using Reinforcement Learning

2026-03-20

AI Summary

This work addresses the misalignment between supervised learning training objectives and reinforcement learning decision goals in financial time series forecasting by proposing an end-to-end fine-tuning framework. The approach first pretrains a predictive model using supervised learning and then fine-tunes it via reinforcement learning, propagating policy gradients back through the original model. This method establishes an effective linkage between supervised pretraining and reinforcement-based fine-tuning, validated across three mainstream reinforcement learning algorithms. Experimental results demonstrate that the fine-tuned models achieve significantly improved performance on trading tasks, while exhibiting strong generalization and cross-market transfer capabilities, thereby offering a practical solution for real-world deployment of financial forecasting systems.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• fine-tuning
• time series prediction
• reinforcement learning
• financial forecasting
• transfer learning

Innovation

• reinforcement learning
• fine-tuning
• time series forecasting
• transfer learning
• financial prediction