FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies

• URL: http://arxiv.org/abs/2603.27450v1
• Date: Sun, 29 Mar 2026 00:02:50 GMT
• Title: FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
• Abstract summary: We introduce a comprehensive taxonomy for algorithms RL with diffusion/flow policies.<n>We provide benchmarks across Gym-Locomotion, DeepMind Control Suite, and IsaacLab.<n>Our work establishes a clear foundation for understanding and algorithm design.
• Score: 51.53039275043005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation. However, efficient reinforcement learning (RL) upon these policies remains a challenge due to the lack of explicit log-probabilities for vanilla policy gradient estimators. While numerous attempts have been proposed to address this, the field lacks a unified perspective to reconcile these seemingly disparate methods, thus hampering ongoing development. In this paper, we bridge this gap by introducing a comprehensive taxonomy for RL algorithms with diffusion/flow policies. To support reproducibility and agile prototyping, we introduce a modular, JAX-based open-source codebase that leverages JIT-compilation for high-throughput training. Finally, we provide systematic and standardized benchmarks across Gym-Locomotion, DeepMind Control Suite, and IsaacLab, offering a rigorous side-by-side comparison of diffusion-based methods and guidance for practitioners to choose proper algorithms based on the application. Our work establishes a clear foundation for understanding and algorithm design, a high-efficiency toolkit for future research in the field, and an algorithmic guideline for practitioners in generative models and robotics. Our code is available at https://github.com/typoverflow/flow-rl.

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