Refinement-based Flow Policy Optimization

• URL: http://arxiv.org/abs/2609.15123v1
• Date: Mon, 14 Sep 2026 06:56:33 GMT
• Title: Refinement-based Flow Policy Optimization
• Abstract summary: Refinement-Based Flow Policy Optimization (RFPO) is a novel framework for training a flow policy in online reinforcement learning.<n>RFPO incorporates Q-guidance into the policy without requiring direct samples from the target distribution.<n>RFPO captures complex multimodal structure without mode collapse.
• Score: 6.752424962104772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flow-based policies offer an expressive representation for online reinforcement learning, but conventional flow matching requires samples drawn from the distribution to be modeled. This poses a challenge when the desired action distribution is defined only implicitly by a Q-function, since directly sampling actions from the resulting distribution is generally intractable. We propose Refinement-Based Flow Policy Optimization (RFPO), a novel framework for training a flow policy in online reinforcement learning by alternating between Q-guided sample refinement and self-target flow matching. RFPO first generates actions from Gaussian noise using the current flow policy and then uses a finite-step stochastic refinement procedure to move them toward an energy-based distribution induced by the Q-function. Each refined action is then paired with its corresponding initial noise sample and used as a fixed target for flow-matching training. By repeatedly refining its own outputs and learning from the resulting targets, RFPO incorporates Q-guidance into the policy without requiring direct samples from the target distribution, while retaining the capacity to represent multiple action modes. We further provide a theoretical analysis of the distributional dynamics induced by RFPO. Across six continuous-control tasks, RFPO matches or outperforms a standard Gaussian-policy baseline on almost every task. Experiments on six synthetic two-dimensional target distributions with diverse geometries demonstrate that RFPO captures complex multimodal structure without mode collapse.

Related papers

• Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent [53.828537014796574]
  Online off-policy reinforcement learning (RL) is shaped by two choices: the policy class and the update rule.<n>We propose MeanFlow Policies, a one-step generative policy class that maps noise to actions through a MeanFlow transformation.<n>Across seven MuJoCo benchmarks, S improves over Gaussian and generative baselines while retaining single-step inference efficiency.
  arXiv  Detail & Related papers  (2026-05-20T15:14:14Z)
• Aligning Flow Map Policies with Optimal Q-Guidance [50.514994916864275]
  Flow map policies are designed for fast action generation by learning to take arbitrary-size jumps.<n>We derive FLOW MAP Q-GUIDANCE (FMQ), a principled closed-form learning target that is optimal for adapting offline flow map policies under a critic-guided trust-region constraint.<n>FMQ achieves state-of-the-art performance in offline-to-online RL, outperforming the previous one-step policy MVP by a relative improvement of 21.3% on the average success rate.
  arXiv  Detail & Related papers  (2026-05-12T17:12:29Z)
• One-Step Generative Modeling via Wasserstein Gradient Flows [56.266175641614986]
  W-Flow is a framework for training a generator that transforms samples from a simple reference distribution into samples from a target data distribution in a single step.<n>We set a new state of the art for one-step ImageNet 256$times$256 generation, achieving 1.29 FID, with improved mode coverage and domain transfer.
  arXiv  Detail & Related papers  (2026-05-12T08:29:44Z)
• Flow Matching Policy with Entropy Regularization [16.47598359293598]
  Flow Matching Policy with Entropy Regularization (FMER) is an Ordinary Differential Equation (ODE)-based online RL framework.<n>FMER parameterizes the policy via flow matching and samples actions along a straight probability path, motivated by optimal transport.<n> Experiments on sparse multi-goal FrankaKitchen benchmarks demonstrate that FMER outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-18T13:00:20Z)
• Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies [4.249024052507976]
  We propose a unified framework, reverse flow matching (RFM), which rigorously addresses the problem of training diffusion and flow models without direct target samples.<n>By adopting a reverse inferential perspective, we formulate the training target as a posterior mean estimation problem given an intermediate noisy sample.<n>We show that existing noise-expectation and gradient-expectation methods are two specific instances within this broader class.
  arXiv  Detail & Related papers  (2026-01-13T01:58:24Z)
• G$^2$RPO: Granular GRPO for Precise Reward in Flow Models [74.21206048155669]
  We propose a novel Granular-GRPO (G$2$RPO) framework that achieves precise and comprehensive reward assessments of sampling directions.<n>We introduce a Multi-Granularity Advantage Integration module that aggregates advantages computed at multiple diffusion scales.<n>Our G$2$RPO significantly outperforms existing flow-based GRPO baselines.
  arXiv  Detail & Related papers  (2025-10-02T12:57:12Z)
• Unleashing Flow Policies with Distributional Critics [15.149475517073258]
  We introduce the Distributional Flow Critic (DFC), a novel critic architecture that learns the complete state-action return distribution.<n>DFC provides the expressive flow-based policy with a rich, distributional Bellman target, which offers a more stable and informative learning signal.
  arXiv  Detail & Related papers  (2025-09-27T03:51:06Z)
• One-Step Flow Policy Mirror Descent [52.31612487608593]
  Flow Policy Mirror Descent (FPMD) is an online RL algorithm that enables 1-step sampling during flow policy inference.<n>Our approach exploits a theoretical connection between the distribution variance and the discretization error of single-step sampling in straight flow matching models.
  arXiv  Detail & Related papers  (2025-07-31T15:51:10Z)
• Flow Matching Policy Gradients [33.011978450108636]
  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.
  arXiv  Detail & Related papers  (2025-07-28T17:59:57Z)
• Sampling from Energy-based Policies using Diffusion [18.135501150108894]
  Energy-based policies offer a flexible framework for modeling complex, multimodal behaviors in reinforcement learning.<n>Existing methods typically use simpler parametric distributions, like Gaussians, for policy representation.<n>We introduce a diffusion-based approach for sampling from energy-based policies, where the negative Q-function defines the energy function.
  arXiv  Detail & Related papers  (2024-10-02T08:09:33Z)
• Learning a Diffusion Model Policy from Rewards via Q-Score Matching [93.0191910132874]
  We present a theoretical framework linking the structure of diffusion model policies to a learned Q-function.<n>We propose a new policy update method from this theory, which we denote Q-score matching.
  arXiv  Detail & Related papers  (2023-12-18T23:31:01Z)
• Implicit Distributional Reinforcement Learning [61.166030238490634]
  implicit distributional actor-critic (IDAC) built on two deep generator networks (DGNs) Semi-implicit actor (SIA) powered by a flexible policy distribution. We observe IDAC outperforms state-of-the-art algorithms on representative OpenAI Gym environments.
  arXiv  Detail & Related papers  (2020-07-13T02:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.