arXiv Machine Learning
Jun 2
Mean Flow Policy Optimization
arXiv:2604. 14698v2 Announce Type: replace Abstract: Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL).
FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
arXiv:2603. 27450v2 Announce Type: replace Abstract: Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation.
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arXiv AI
Jun 9
Reinforcement Learning for Flow-Matching Policies with Density Transport
arXiv:2606. 08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems.
arXiv Machine Learning
Sep 15
VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching
arXiv:2609.14261v1 Announce Type: cross Abstract: Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative...
arXiv Machine Learning
Jun 30
FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification
arXiv:2606. 30376v1 Announce Type: new Abstract: Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods.
arXiv Machine Learning
Jun 2
Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.
arXiv AI
2d ago
Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies
arXiv:2610.01882v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment....
arXiv Machine Learning
Jun 8
GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios
arXiv:2606. 06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks.
arXiv AI
Jun 10
Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
arXiv:2606. 10613v1 Announce Type: cross Abstract: Diffusion-based Q-learning has emerged as a powerful paradigm for offline reinforcement learning, but its reliance on multi-step denoising makes both training and inference computationally expensive and brittle.
arXiv AI
Jun 16
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.
arXiv Machine Learning
Jul 13
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
arXiv:2607. 08877v1 Announce Type: cross Abstract: Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks.
arXiv Computer Vision
Aug 21
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
arXiv AI
Jul 14
VINE: Taming Generative Control Policies for Reinforcement Learning
arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.