FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance

Traditional stochastic control methods in finance rely on simplifying assumptions that often fail in real-world markets. While these methods work well in specific, well-defined scenarios, they underperform when market conditions change. We introduce \textbf{FinFlowRL}, a novel framework for financial stochastic control that combines imitation learning with reinforcement learning. The framework first pre-trains an adaptive meta-policy by learning from multiple expert strategies, then fine-tunes it through reinforcement learning in the noise space to optimize the generation process. By employing action chunking—generating sequences of actions rather than single decisions—it addresses the non-Markovian nature of financial markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.