FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 29 references that are in the map. First posted on arXiv 30 Aug 2025.
Builds on
8 of 29 references.
References · 8
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2022Flow Matching for Generative Modelingcited by 5,702
- 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
- 2025Mean Flows for One-step Generative Modelingcited by 561
- 2024Flow Matching Guide and Codecited by 301
- 2025Steering Your Diffusion Policy with Latent Space Reinforcement Learningcited by 169
- 2025Reinforcement Learning with Action Chunkingcited by 107
- 2025Flow-Based Policy for Online Reinforcement Learningcited by 37
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- 2022MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization
- 2023PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets
- 2024Reinforcement Learning for Jump-Diffusions, with Financial Applications
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