FM-IRL: Flow-Matching for Reward Modeling and Policy Regularization in Reinforcement Learning
arXiv:2510.09222v3 Announce Type: replace Abstract: Flow Matching (FM) has shown remarkable ability in modeling complex distributions and achieves strong performance in offline imitation learning for cloning expert behaviors. However, despite its behavioral cloning expressiveness, FM-based policies are inherently limited by their lack of environmental interaction and exploration. This leads to poor generalization in unseen scenarios beyond the expert demonstrations, underscoring the necessity of online interaction with environment. Unfortunately, optimizing FM policies via online interaction i
This paper combines Flow Matching with reinforcement learning, addressing the limitations of prior FM-based methods that lacked environmental interaction, a critical next step for practical AI policy development.
Advanced techniques for reward modeling and policy regularization are crucial for developing more robust and generalizable AI agents capable of complex tasks and real-world interaction.
The ability to integrate environmental interaction and exploration into Flow Matching policies allows for less brittle and more adaptive AI systems, moving beyond static behavioral cloning.
- · AI researchers
- · Reinforcement learning platforms
- · Robotics developers
- · AI systems relying solely on behavioral cloning
- · Traditional model-free reinforcement learning approaches
Improved performance and generalization of AI agents in dynamic environments.
Accelerated development of AI systems capable of learning from partial demonstrations and real-world feedback.
Potentially enables more sophisticated robotic control and autonomous systems that can adapt to unforeseen circumstances.
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