arXiv:2610.16275v1 Announce Type: cross Abstract: Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling express...
arXiv:2610.29075v1 Announce Type: new Abstract: We aim to address the problem of Out-of-Distribution (OOD) Mode Averaging, a critical problem that emerges when distilling Flow Matching (FM) based beh...
arXiv:2609.15883v1 Announce Type: cross Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies...
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:2606. 29820v1 Announce Type: cross Abstract: In complex continuous-control reinforcement learning tasks, multimodal optimal actions often coincide with uncertain, multimodal return distributions, making reliable value estimation and multimodal exploration challenging.
The paper introduces AnchorQ, a method that improves sample-efficient reinforcement learning for policy refinement by using flow-reversed action anchors. AnchorQ maps reference behaviors to state-conditioned latent anchors, samples local particles around these anchors, and employs value-guided sampling to explore promising refinements. Experiments on simulated and real-world manipulation tasks show that AnchorQ outperforms state-of-the-art post-training baselines, achieving a 37% higher real-world success rate and 97% greater simulated task throughput within the same interaction budget.
The paper introduces OMAF, an Online MARL framework that uses a one-step Transformer-based flow policy to generate coordinated actions efficiently. By replacing costly iterative sampling with a single-step action generation and a joint optimization scheme that couples softmax Q-value estimation with flow policy objectives, OMAF maintains expressive multimodal behavior while improving training speed. Experiments on 10 tasks from MPE and MAMuJoCo demonstrate up to 3.4× higher returns and 10.5× better sample efficiency compared to baseline methods.
arXiv:2510. 09976v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models such as OpenVLA, Octo, and $\pi_0$ have shown strong generalization by leveraging large-scale demonstrations, yet their performance is still fundamentally constrained by the quality and coverage of supervised data.
The paper introduces fixed‑point flow matching (FFM), a data‑free method for training and fine‑tuning flow policies in reinforcement learning. FFM formulates the actor update as a fixed‑point iteration using a flow‑matching regression objective, where the critic’s action gradients act as targets. Experiments show that FFM can effectively fine‑tune pretrained flow policies in offline‑to‑online scenarios and train flow policies from scratch in online RL, establishing it as a principled approach for learning expressive flow‑based policies without training data.
arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.
arXiv:2601. 08136v2 Announce Type: replace Abstract: Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.
arXiv:2609.38855v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models based on generative frameworks, such as Flow Matching, have recently achieved impressive performance in robotic m...