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

By Yang Li, Zhi Chen, Steve Y. Yang, Ruixun Zhang

Published 2025-09-22

Everscope rating

1553.6

Relevance to quantitative trading

9 / 10

Implementation complexity

8 / 10

Reproducibility

3 / 5

About this paper

Methodology: FinFlowRL (Two-Stage Imitation-Reinforcement Learning with Flow Matching). Problem types: Market Making, Reinforcement Learning, Optimization, Algorithmic Execution, Risk Management, Generative Modeling, Transfer Learning.

arXiv:2509.17964 · Paper rankings

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