arXiv Machine Learning June 9, 2026 By Lingkai Kong, Anagha Satish, Hezi Jiang, Akseli Kangaslahti, Andrew Ma, Wenbo Chen, Mingxiao Song, Lily Xu, Milind Tambe

Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions

Read the original on arXiv Machine Learning →

arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

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Action abstractions for amortized sampling

The paper introduces a method that integrates action abstraction into policy optimization for reinforcement learning and generative flow networks. By iteratively identifying frequently used action subsequences in high‑reward trajectories and treating them as single high‑level actions, the approach expands the action space and improves sample efficiency. Experiments on synthetic and real‑world tasks show that this technique discovers diverse high‑reward states more effectively, especially on challenging exploration problems, and yields interpretable abstract actions that reflect the underlying reward structure.

By Oussama Boussif, L\'ena N\'ehale Ezzine, Joseph D Viviano, Micha{\l} Koziarski, Moksh Jain, Esmeralda S. Whitammer, Emmanuel Bengio, Rim Assouel, Yoshua Bengio

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arXiv Machine Learning

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VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching

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diffusion reinforcement-learning robotics efficiency multimodal

arXiv AI

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Reinforcement Learning for Flow-Matching Policies with Density Transport

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diffusion reinforcement-learning robotics fine-tuning efficiency multimodal safety

arXiv AI

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Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport

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By Jaehun Shon, Jinha Choi, Jongwook Jeon, Jongmin Lee

reinforcement-learning efficiency multimodal benchmarks safety

arXiv AI

Jul 23

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

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llms agents reinforcement-learning benchmarks

arXiv AI

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arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.

By Rushuai Yang, Zhuo Han, Houlin Li, Hecheng Wang, Zhichao Wu, Rui Zhang, Zhaowei Zhang, Zihong Chen, Xiaohan Yan, Chiming Liu, Yi Chen, Wei Shan, Maoqing Yao

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