PolicyFlow: Policy Optimization with Continuous Normalizing Flow in Reinforcement Learning

• URL: http://arxiv.org/abs/2602.01156v1
• Date: Sun, 01 Feb 2026 11:08:09 GMT
• Title: PolicyFlow: Policy Optimization with Continuous Normalizing Flow in Reinforcement Learning
• Authors: Shunpeng Yang, Ben Liu, Hua Chen,
• Abstract summary: PolicyFlow is a novel on-policy CNF-based reinforcement learning algorithm.<n>It integrates expressive CNF policies with PPO-style objectives without requiring likelihood evaluation along the full flow path.<n>PolicyFlow approximates importance ratios using velocity field variations along a simple path, reducing computational overhead without compromising training stability.
• Score: 6.836651088754774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Among on-policy reinforcement learning algorithms, Proximal Policy Optimization (PPO) demonstrates is widely favored for its simplicity, numerical stability, and strong empirical performance. Standard PPO relies on surrogate objectives defined via importance ratios, which require evaluating policy likelihood that is typically straightforward when the policy is modeled as a Gaussian distribution. However, extending PPO to more expressive, high-capacity policy models such as continuous normalizing flows (CNFs), also known as flow-matching models, is challenging because likelihood evaluation along the full flow trajectory is computationally expensive and often numerically unstable. To resolve this issue, we propose PolicyFlow, a novel on-policy CNF-based reinforcement learning algorithm that integrates expressive CNF policies with PPO-style objectives without requiring likelihood evaluation along the full flow path. PolicyFlow approximates importance ratios using velocity field variations along a simple interpolation path, reducing computational overhead without compromising training stability. To further prevent mode collapse and further encourage diverse behaviors, we propose the Brownian Regularizer, an implicit policy entropy regularizer inspired by Brownian motion, which is conceptually elegant and computationally lightweight. Experiments on diverse tasks across various environments including MultiGoal, PointMaze, IsaacLab and MuJoCo Playground show that PolicyFlow achieves competitive or superior performance compared to PPO using Gaussian policies and flow-based baselines including FPO and DPPO. Notably, results on MultiGoal highlight PolicyFlow's ability to capture richer multimodal action distributions.

Related papers

• ExO-PPO: an Extended Off-policy Proximal Policy Optimization Algorithm [2.6813717321945103]
  We propose a new PPO variant based on the stability guarantee from conservative on-policy iteration with a more efficient off-policy data utilization.<n>Compared with PPO and some other state-of-the-art variants, we demonstrate an improved performance of ExO-PPO with balanced sample efficiency and stability on varied tasks.
  arXiv  Detail & Related papers  (2026-02-10T12:29:57Z)
• Rethinking the Trust Region in LLM Reinforcement Learning [72.25890308541334]
  Proximal Policy Optimization (PPO) serves as the de facto standard algorithm for Large Language Models (LLMs)<n>We propose Divergence Proximal Policy Optimization (DPPO), which substitutes clipping with a more principled constraint.<n>DPPO achieves superior training and efficiency compared to existing methods, offering a more robust foundation for RL-based fine-tuning.
  arXiv  Detail & Related papers  (2026-02-04T18:59:04Z)
• A Step Back: Prefix Importance Ratio Stabilizes Policy Optimization [58.116300485427764]
  Reinforcement learning post-training can elicit reasoning behaviors in large language models.<n> token-level correction often leads to unstable training dynamics when the degree of off-policyness is large.<n>We propose a simple yet effective objective, Minimum Prefix Ratio (MinPRO)
  arXiv  Detail & Related papers  (2026-01-30T08:47:19Z)
• Dichotomous Diffusion Policy Optimization [46.51375996317989]
  DIPOLE is a novel RL algorithm designed for stable and controllable diffusion policy optimization.<n>We also use DIPOLE to train a large vision-language-action model for end-to-end autonomous driving.
  arXiv  Detail & Related papers  (2025-12-31T16:56:56Z)
• 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)
• Relative Entropy Pathwise Policy Optimization [66.03329137921949]
  We present an on-policy algorithm that trains Q-value models purely from on-policy trajectories.<n>We show how to combine policies for exploration with constrained updates for stable training, and evaluate important architectural components that stabilize value function learning.
  arXiv  Detail & Related papers  (2025-07-15T06:24:07Z)
• EXPO: Stable Reinforcement Learning with Expressive Policies [74.30151915786233]
  We propose a sample-efficient online reinforcement learning algorithm to maximize value with two parameterized policies.<n>Our approach yields up to 2-3x improvement in sample efficiency on average over prior methods.
  arXiv  Detail & Related papers  (2025-07-10T17:57:46Z)
• BNPO: Beta Normalization Policy Optimization [9.60676665395923]
  We propose a novel policy optimization method that adaptively normalizes rewards using a Beta distribution with dynamically updated parameters.<n>We provide theoretical analysis demonstrating BNPO's variance-reducing properties and show that it generalizes both REINFORCE and GRPO under binary-valued reward settings.<n> Experimental results confirm that BNPO achieves state-of-the-art performance among policy optimization methods on reasoning tasks.
  arXiv  Detail & Related papers  (2025-06-03T13:28:57Z)
• Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization [55.97310586039358]
  Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality.<n>We propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO)<n>Specifically, we introduce the Q-weighted variational loss, which can be proved to be a tight lower bound of the policy objective in online RL under certain conditions.<n>We also develop an efficient behavior policy to enhance sample efficiency by reducing the variance of the diffusion policy during online interactions.
  arXiv  Detail & Related papers  (2024-05-25T10:45:46Z)

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.