Flow Matching Policy Gradients

• URL: http://arxiv.org/abs/2507.21053v2
• Date: Fri, 01 Aug 2025 13:04:28 GMT
• Title: Flow Matching Policy Gradients
• Authors: David McAllister, Songwei Ge, Brent Yi, Chung Min Kim, Ethan Weber, Hongsuk Choi, Haiwen Feng, Angjoo Kanazawa,
• Abstract summary: Flow Policy Optimization casts policy optimization as maximizing an advantage-weighted ratio computed from the conditional flow matching loss.<n>We show that FPO can train diffusion-style policies from scratch in a variety of continuous control tasks.<n>We find that flow-based models can capture multimodal action distributions and achieve higher performance than Gaussian policies, particularly in under-conditioned settings.
• Score: 33.011978450108636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization (FPO), a simple on-policy reinforcement learning algorithm that brings flow matching into the policy gradient framework. FPO casts policy optimization as maximizing an advantage-weighted ratio computed from the conditional flow matching loss, in a manner compatible with the popular PPO-clip framework. It sidesteps the need for exact likelihood computation while preserving the generative capabilities of flow-based models. Unlike prior approaches for diffusion-based reinforcement learning that bind training to a specific sampling method, FPO is agnostic to the choice of diffusion or flow integration at both training and inference time. We show that FPO can train diffusion-style policies from scratch in a variety of continuous control tasks. We find that flow-based models can capture multimodal action distributions and achieve higher performance than Gaussian policies, particularly in under-conditioned settings.

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