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Flow Matching Policy Gradients
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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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Cited by 37 Pith papers
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Training Diffusion Policies via Prior-Mapping Co-Evolution
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Driving Intents Amplify Planning-Oriented Reinforcement Learning
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Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT
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FAIL: Flow Matching Adversarial Imitation Learning for Image Generation
Post-training of flow matching can be framed as adversarial imitation learning, and the proposed FAIL methods improve FLUX's generation quality using 13K expert images without preference pairs.
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ReFPO: Reflow Regularization for Flow Matching Policy Gradients
ReFPO adds explicit Reflow regularization to FPO, stabilizing PPO-style training and supporting high-fidelity one-step inference across GridWorld, MuJoCo, and Humanoid tasks.
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